Xidian University Liu Congfeng Page 1 of 22

Size: px
Start display at page:

Download "Xidian University Liu Congfeng Page 1 of 22"

Transcription

1 Rdom Sgl rocessg Chpter Expermets d robblty Chpter Expermets d robblty Cotets Expermets d robblty.... Defto of Expermet..... The Smple Spce..... The Borel Feld The robblty Mesure...3. Combed Expermets Crtes roduct of Two Expermets Crtes roduct of Expermets Coutg Expermets Selecto Combed Expermet Codtol robblty Totl robblty Theorem Byes's Theorem Rdom ots Uform Rdom ots Itervl Nouform Rdom ots Itervl....5 Summry... Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge of

2 Rdom Sgl rocessg Chpter Expermets d robblty Expermets d robblty. Defto of Expermet To fully pprecte the meg of probblty d cqure strog mthemtcl foudto for lytcl wor, t s ecessry to defe precsely the cocept of expermet d smple spce mthemtclly. These deftos provde cosstet methods for the ssgmet of elemetry probbltes prdoxcl stutos, d thus llow for megful clculto of probbltes of evets other th the elemetry evets. Although t the begg ths pproch my seem stlted, t wll led to cocrete cocept of probblty d terpretto of derved probbltes. A expermet E s specfed by the three tuple S, F,., where S s fte, coutble, or ocoutble set clled the smple spce, F s Borel feld specfyg set of evets, d probblty mesure llowg clculto of probbltes of ll evets. s.. The Smple Spce The smple spce S s set of elemets clled outcomes of the expermet E d the umber of elemets could be fte, coutble, or ocoutble fte. For exmple, S could be the set cotg the sx fces of de, S f, f, f 3, f 4, f 5, f 6, or the postve tegers, :,,... or the rel vlues betwee zero d oe, S x : 0 x, respectvely. S, A evet s defed s y subset of S. O sgle trl of the expermet outcome s obted. If tht outcome s member of evet, t s sd tht the evet hs occurred. I ths wy my dfferet evets occur t ech trl of the expermet. For exmple, f f s the outcome of sgle trl of the expermet the the evets f, f, f, f, f 3,..., f, f 6,..., f, f 3, f 5,, ll occur. Evets cosstg of sgle elemets, le f, re clled elemetry evets. The mpossble evet correspods to the empty set d ever occurs, whle the cert evet, S, cots ll outcomes d thus lwys occurs o mtter wht the outcome of the trl s. Evets A d B re clled mutully exclusve or dsjot f A B, where s the ull set. Two evets A d B re clled depedet f A B A B. The evets A, A,, A re defed to be depedet f the probbltes of ll tersectos two, three,..., d evets c be wrtte s products. Ths mples for ll, j,,, tht the followg codtos must be stsfed for depedece Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge of

3 Rdom Sgl rocessg Chpter Expermets d robblty A Aj A Aj A A A A A A j A A A A A... A... j (.).. The Borel Feld A feld c be defed s oempty clss of sets such tht () f d () f F d b F the b F F, the the complemet of F. Thus feld cots ll fte uos, d by vrtue of complmets d DeMorg's theorem, ll tersectos of the collecto. If we further requre tht ll fte uos d tersectos re preset the collecto, Borel feld s defed. The set of ll evets of our expermet tht wll hve probbltes ssged to them (mesurble evets) must be Borel feld to hve mthemtcl cosstecy. If A, collecto of evets, hs fte umber of elemets, Borel feld c be formed s the set of those evets plus ll possble subsets obted by uos d tersectos of those evets cludg the ull set d etre set S. If set s ocoutble, t s lttle hrder to descrbe Borel feld. The most commo Borel feld, cotg the rel umbers, s the smllest Borel feld cotg the followg tervls: x : x x for ll x rel umbers. Ths wll cot ll fte d fte closed d ope tervls of the form, d b [, b],[, b),(, b] those tervls thereof.,, where d b re rel umbers d the tersectos d uos of..3 The robblty Mesure The probblty mesure, codtos re stsfed () For y evet F, must be cosstet ssgmet of probbltes such tht the followg A, the probblty of the evet A, A, s such tht A 0 () For the cert evet, S, S. (3) If A d B re y two evets such tht B (3) If.. A the A B A B A F for,,, d A for ll j, the A j A A A A A A. Wth these codtos stsfed, the probblty of y evet A F c be clculted. How does oe go bout ssgg probbltes such tht we stsfy the codtos bove? For the cse where S s set wth fte umber of elemets the codtos bove c be stsfed Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 3 of

4 Rdom Sgl rocessg Chpter Expermets d robblty by ssgg probbltes to ll the evets wth oly sgle outcomes, e, where e S, such tht codtos () d () bove re stsfed. Ths mppg from the smple spce S to the postve rels s clled the dstrbuto fucto for the probblty mesure d equvletly specfes the probblty mesure. Whe S s set wth ocoutbly fte umber of elemets the ssgmet bove s ot useful s the probbltes of most elemetry evets wll be zero. I ths cse the ssgmet of probbltes s cosstet f the probbltes of the evets x : x x for ll x rel umbers re ssged such tht () 0 x : x x () x : x x x x x (bouded). d (3) lm s 0 : for ll x x (odecresg fucto of x ) of x : x x equls x x Ths mppg: x x x : x : x (cotuous from the rght sde)., defed for ll x, s clled the cumultve dstrbuto fucto d equvletly descrbes the probblty mesure for the ocoutble cse. From ths dstrbuto fucto we re ble to clculte ll probbltes of evets tht re members of the Borel feld F. The cumultve dstrbuto fucto could hve lso bee used to specfy the probblty mesure for the cse where S hs coutble or fte umber of elemets. A umber of exmples of expermets wll ow be preseted. They wll clude couple of co-tossg expermets d de-rollg expermet. The expermets wll be descrbed by specfyg ther smple spce, Borel feld, d probblty mesures. Exmple. Ths expermet cossts of sgle flppg of co tht results ether hed or tl showg. Gve ts descrpto by specfyg s S, F,. Soluto The possble outcomes of the expermet re ether hed or tl. Thus the smple spce c be descrbed s the set S hed, tl. The Borel feld F cossts of the elemetry evets hed d tl, the ull set, d S. To complete the descrpto of the expermet, probblty mesure must be ssged. Ths prtculr ssgmet could be bsed o prevous experece, creful expermetto, use of fvorble to totl ltertves, or y other terpretto of the cocept of probblty. There s o rght or wrog ssgmet, but cert ssgmets (models) my be more pproprte explg the results of correspodg physcl expermets. For the purpose of ths exmple, we ssume tht ths co hs bee tmpered wth for more ofte hed comes up th tl, whch we specfy by hed p d tl p. These two ssgmets comprse the dstrbuto fucto d thus the probblty mesure for the Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 4 of

5 Rdom Sgl rocessg Chpter Expermets d robblty expermet. Exmple. A more relstc ssgmet for the expermet of flppg co could be the expermet defed s follows: S hed, tl, edge wth descrbed by h S, F,, t 0. 49, e The Borel feld F s defed s the power set of S. () Idetfy ll the evets (elemets) of the Borel feld. (b) Clculte the probbltes of those evets. Soluto () The Borel feld F specfyg the mesurble evets s the power set of S (ll possble subsets of S ) gve by h, t, e, h, t, h, e, t, e, h, t e F,, Where s the mpossble evet, d h, t, e s the cert evet. The other evets cosst of ll possble proper subsets of S, sgle elemets, d combtos of two elemets. (b) By defto 0 d the probbltes of the elemetry evets re gve the specfcto of the probblty mesure of the expermet s h 0. 49, t 0. 49, d e The probbltes of the other evets c be determed by repeted pplcto of property (3) for the probblty mesure. For exmple the evets h d e re mutully exclusve therefore h, e c be foud s follows: h, e h e h e Smlrly h, t 0. 98, t, e 0. 5, d h, t, e.. Combed Expermets Combed expermets ply mportt role probblty theory pplctos. There re my wys we c combe expermets, cludg crtes products whch depedet trls of the sme or dfferet expermets c be descrbed. I some cses the probbltes of evets wll deped o the results of prevous trls of expermets or rdom selecto of dfferet expermets. A umber exmples of combed expermets re ow explored begg wth the clsscl cse of smplg wth replcemet... Crtes roduct of Two Expermets Cosder the cse of hvg two seprte expermets specfed by the followg:. E : S, F. d Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 5 of

6 Rdom Sgl rocessg Chpter Expermets d robblty. E : S, F.. The smple spces S d S re usully dfferet sets, for exmple, results of co toss d results of de roll, but they could be the sme sets represetg seprte trls of the sme expermet s repeted co-tossg expermets. We c defe ew combed expermet by usg the crtes product cocept s E E E, where the ew smple spce S S S s the crtes product of the two smple spces expressble by the ordered pr of elemets where the frst elemet s from S d the secod s from S. Exmple.4 Let expermet E : S, F, d E : S, F, be defed s follows: E s the expermet of flppg co wth outcomes hed h d tl t wth equl probblty of occurrece. E s the expermet of rdom selecto of colored bll from box wth outcomes red r, whte w, d blue E E E E b wth replcemet. Defe the ew expermet E s wth expermet E d beg performed depedetly of ech other; tht s, the outcome of expermet E o wy effects the outcome of E, d vce vers. Set up resoble model for ths ew expermet. Soluto To specfy the model t suffces to gve S, F, of the ew expermet E. The ew smple spce S s the Crtes product of the two expermets d gve by S S S. S h, t d the secod from Elemets of S re ordered prs wth the frst elemet comg from S r, w, b ; therefore h, r, h, w, h, b, t, r, t, w, t b S, The ew Borel feld F s selected s the power set of S, tht s ll possble uos d tersectos of S. Ths cludes the ull set, the etre set, d ll possble prs, trples, d so o, s show below: F The probblty mesure, h, r, h, w, h, b, t, r, t, w, t, b h, r, h, w, h, r, h, b, h, r, t, r, h, r, t, w,... h, r, h, w, h, b, h, r, h, w, t, r, h, r, h, w, t, w,... h, r, h, w, h, b, t, r, t, w, t, b c be descrbed by specfyg the probblty of the elemetry evets. Oce these re ow, the probblty of y evet c be foud by wrtg the evet s uo of those evets d usg property (3). Sce the expermets re depedet, t s resoble to ssg probbltes of the elemetry evets s product of the probbltes from ech expermet. For exmple, h r h r,. If hed d tl re eqully probble E the t s resoble for to be Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 6 of

7 Rdom Sgl rocessg Chpter Expermets d robblty descrbed by h t could be gve by r 0. 5, w 0. 3, b 0. probble E, the exmple, the probblty mesure evets (the dstrbuto fucto):, d f we hve reso to beleve tht red, whte, d blue re ot eqully. Thus, for ths c be descrbed by specfyg the probbltes of the elemetry h, r h r 0.5 t, r t r h, w h w 0.5 t, w t w h, b h b 0. t, b t b 0. The evet of hed the ew expermet s H h, r, h, w, h, b occurrece c be determed usg property (3) s H h, r, h, w, h, b h, r h, w h, b , d ts probblty of.. Crtes roduct of Expermets Cosder the cse of hvg seprte expermets specfed by Defe ew combed expermet where the ew smple spce S S S.. E : S, F, for,,,. E : S, F, s crtes product: E E E E S s the crtes product of the spces d expressble by the ordered -tuples of elemets whose frst elemet s from S d the secod s from S, the th from S. The E re, geerl, dfferet, but my cses the expermet could be formed from depedet trls of the sme expermet. Also there s mportt clss of problems where the expermets re the sme, yet they cot be thought of s depedet. A good exmple of ths s the rdom selecto of outcomes wthout replcemet. Exmples of ech type re ow preseted. Boml Dstrbuto. Cosder the expermet : S, F,. re ether flure dcted by 0 or success dcted by ; therefore S 0, probblty mesure. flure p E E E where the outcomes of the expermet. Assume tht the for the expermet s gve by success p F s the set, 0,, 0, d 0 d tht the. Defe ew expermet by E E where E : S, F,. descrbes the ew expermet. Assume tht ths represets depedet trls of the sme expermet E where the probblty of success or flure s the sme for ech trl. The S, F,. re ow descrbed for ths ew expermet. The ew S s the crtes product Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 7 of

8 Rdom Sgl rocessg Chpter Expermets d robblty S S d cossts of ll possble -tuples where the elemets re ether 0 or s show below: S S 0, 0, 0, 0 0, 0, 0, 0, 0,, 0 S (.) 0, 0,,,,, The ew probblty mesure s specfed oce the dstrbuto fucto or probbltes of the elemets of S re determed. By vrtue of the depedet expermet ssumpto, the probblty of ech elemetry evet of the ew expermet s the product of the probbltes for the elemetry evets the sgle trl. For exmple, the 0,,,0,,0,, s gve by 4 4 0,,,0,,0,, p p (.3) As mtter of fct every sequece tht hs oly four oes (successes) wll hve ths sme probblty. The totl umber of these sequeces s the combto of thgs te four t tme, sce we hve loctos d we wt oly four of them wth oes. The probblty dstrbuto fucto for the ew expermet c the be determed s follows, where the frst etry s the elemetry sequece d fter the rrow s the correspodg probblty: Outcome robblty 0,0,,0,0 0,0,,0, ,0,,0, 0,0,,0, ,0,,,0 0,0,,, ,0,,, 0,0,,, p ( p) p ( p) p ( p) p ( p) 0,,,,,,,, p ( p) The Borel feld wll be the power set ssocted wth S d specfes ll evets for whch probbltes re ssged. robbltes of dfferet type of evets for the bove exmple c be determed by usg the dstrbuto fucto descrbed. There re wde umber of pplctos s success c me ll ds of thgs. For exmple, success could be obtg ce drwg crd from stdrd dec of crds wth replcemet, or obtg successful recepto of bry symbol from rdom commucto chel. The evet of exctly successes out of depedet trls ppers frequetly physcl stutos d ts probblty wll ow be derved usg the results bove. Defe the evet A s the evet of exctly oe success out of trls. From the results bove we see tht the probblty of exctly oe success out of trls s (.4) Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 8 of

9 Rdom Sgl rocessg Chpter Expermets d robblty A exctly oe success out of trls 0,0,,0, 0,0,,,0,0,,0,0 0,0,,0, 0,0,,,0,0,,0,0 p p Smlrly the probblty of exctly successes out of trls c be foud to be A exctly success out of trls p p (.6) I some problems we my wsh to ow the probblty tht the umber of successes out of trls s wth rge of vlues. The probblty tht the umber of successes s the rge (.5) m c be obted by ddg the probbltes of exctly successes for tht rge, sce the evets A d A j re mutully exclusve evets for ll d j such tht j. Therefore J umber of successes K exctly J exctly J successes successes K K A p p J J exctly K successes (.7) Exmple.5 Cosder the expermet of tossg fr co wth the smple spce S h, t dstrbuto h 0. 6 d t 0. 4 d probblty. Suppose the expermet s performed 0 tmes depedetly to defe ew crtes product expermet. () Determe the probblty tht we get exctly 5 heds the 0 trls. (b) Determe the probblty tht we get greter th 7 heds the 0 trls. (c) Determe the probblty tht we get less th or equl 9 heds the 0 trls. (d) Determe the probblty tht the umber of tls s greter th or equl to 4 d less th or equl 5. Soluto The desred probbltes re determed for Eqs. (.6) d (.7) s follows: () A exctly 5 heds out of 0 trls (b) umber exctly exctly exctly 8 0 of heds 8 heds 9 heds 0 heds Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 9 of

10 Rdom Sgl rocessg Chpter Expermets d robblty (c) less th or equl 9 heds out of 0 trls exctly 0 heds out of 0 trls umber exctly exctly (d) 4 5 of heds 4 heds 5 heds Approxmtos for Boml robbltes. The probbltes of successes trls of Beroull expermet ws foud to be s Eq. (.6) d evlutg probbltes of rges of successes s success out of trls s plotted for vlues of gve Eq. (.7). I Fgure. the equl to 5, 0, d 50 for ll vlues of, d success The grphs show tedecy towrd hll-shped curve smlr to Guss fucto. These plots would be symmetrcl roud the pot 0.5 f p However, whe p does ot equl 0.5, s for the cses gve, the plot s ot qute symmetrcl. If the umber of trls s lrge, the clculto lod due to the fctorls s cosderble, d cert pproxmtos to these probbltes become useful. My of these pproxmtos re good provded tht the umber of trls, umber of successes, d probblty of success p stsfy gve set of codtos. Of these pproxmtos we wll preset the DeMovre d osso pproxmtos d the regos where the pproxmtos re relble. DeMovre-Lplce Approxmto. For p p p p d p p exp p( p) p p of the order of p p, (.8) osso Approxmto. For, p, d p of order, p p e ( p) p! (.9) Approxmto of Regos of Successes Beroull Trls. Sy tht we re terested pproxmtg the probblty tht the umber of successes repeted trls of Beroull expermet s the Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 0 of

11 Rdom Sgl rocessg Chpter Expermets d robblty rge. If ths rge of successes cots vlues tht stsfy the DeMovre-Lplce pproxmto, the the summto c be pproxmted by usg the error fucto or the (x) fucto s follows: p p p (.0) p p( p) p( ) p x y / where ( x) e dy The (x) c oly be determed by umercl tegrto s there s o tdervtve d t s coveet to use the tble gve some boo ppedx cotets. 0.4 ( successes) 0.3 ( successes) N=5, p=0.6, Bom (,5) N=0, p=0.6, Bom (,0) 0. ( successes) N=50, p=0.6, Bom (,50) Fgure. lot of ( successes out of trls) for p=0.6 d =5,0, 50. Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge of

12 Rdom Sgl rocessg Chpter Expermets d robblty Multoml Dstrbuto. A combed expermet tht s exteso of the Beroull trl expermet just descrbed, whch resulted the boml dstrbuto, s the cse of multple occurreces of severl dfferet evets o multple trls of the sme expermet. Cosder expermet E : S, F,.. Defe set of evets A, to, cosstg of elemets of S such tht ther uo s the cert evet, they re prwse dsjot, d ther probbltes re gve by A p for to. descrbed by Defe ew expermet E by E E E E E : S, F,.. Assume tht ths represets depedet trls of the sme expermet E. Let, to, be the umber of tmes evet A occurs the ew expermet, whch s composed of repeted trls. Also ssume tht 0 d. It c be show tht the probblty tht A occurs exctly tmes the trls s A A A occurs occurs occurs exctly exctly exctly tmes, tmes,! p!!! tmes, p p (.5) Hypergeometrc Dstrbuto. I the prevous compoud expermets the trls of the expermet were cosdered to be depedet. A very mportt clss of expermetto problems s smplg doe wthout replcemet, for fte umber of elemets, d wthout depedet trls. For exmple, f s the umber of successful s elemets d b s the umber of flure f elemets d elemets re drw t rdom but ot replced, the correspodg outcomes of the expermet do ot cot ll possble -tuples of s d f, So the -tuple s, s,, s d sequeces cotg more th successes re ot possble. It c be show for ths expermet tht the probblty of successes out of trls c be determed s b ( ) successes trls, 0,, (.6) b The ext exmple s typcl of the type of problems for whch the hypergeometrc formul bove c be used. Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge of

13 Rdom Sgl rocessg Chpter Expermets d robblty..3 Coutg Expermets A specl clss of compoud expermets del wth successve pplcto of expermets where the totl expermet my be stopped t y pot depedg o the results from the curret expermet. Two commo dstrbutos result from these compoud expermets. Geometrc Dstrbuto. The geometrc dstrbuto s result of other form of compoud expermet. Defe coutble umber of detcl expermets E S, F,. for,,. Ech of these : expermets wll be of the Beroull type (two possble outcomes) prevously dscussed, wth detcl probbltes of success; the result of the expermet, f performed, s depedet of prevous or future expermets. The ew expermet s descrbed s follows: erform expermet E f success s obted, the stop; f success does ot hppe, the perform expermet E ; f success occurs, the stop otherwse. Cotue ths process utl success occurs. Thus the umber of trls s ot the sme ech tme the compoud expermet s performed, d thus the smple spce s ot crtes product. If p s the probblty of success o ech expermet, the the smple spce, Borel feld d probblty mesure c be descrbed s follows: The elemetry evets re sequeces of 's (for success) d 0's for flures but wth the property tht they ed o d oly hve 0's precedg the. Thus S c be descrbed by Notce tht,0, 0,,0,,,., 0,, 0,0,,, 0,0,,0,, S (.7) etc, re ot elemets of the smple spce sce the ew expermet would stop t d ot proceed to the ext oe. Evets tht re collectos of outcomes re ot of the sme sequece legth. For exmple,, 0, s evet where success s obted less th or equl to trls. The probblty mesure. c be specfed by gvg the dstrbuto fucto for the elemetry evets. The s the probblty of gettg success o the very frst trl. Sce the probblty of success o the frst expermet s p, the probblty of gettg s lso p, p (.8) The probblty of gettg 0, equls the probblty of gettg flure o performg the frst expermet d gettg success o the performce of the secod expermet. Sce results of ech expermet re depedet f performed, the 0, c be wrtte s product:, pp 0 (.9) Smlrly the probblty of the elemetry evet cosstg of sequece of flures (0's) followed by sgle success () s Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 3 of

14 Rdom Sgl rocessg Chpter Expermets d robblty These probbltes s mesure 0,0, 0, p zeros determe the dstrbuto fucto, or equvletly the probblty.. It c be show tht p (.0) p p (.) The Borel feld s g defed s the power set of S, tht s, ll possble subsets of S. The probbltes of rbtrry evets tht re the subsets of S c the be foud by ddg up the probbltes of the elemetry elemets the evet. For exmple, defe the evet A to be the set of outcomes such tht success occurs o the performce of odd umber of expermets. Thus A c be wrtte s, 0,0,, 0,0,0,0,,, 0 A,, (.) The probblty of A c be determed by ddg up the probbltes of the elemetry evets d usg the propertes of geometrc sequece s follows. A, 0,0,, 0,0,0,0,,, 0 p p p 0,, p p (.3) Negtve Boml Dstrbuto. A exteso of the geometrc expermet s compoud expermet whch the umber of trls ecessry to obt successes s desred rther th the umber of trls eeded for sgle success. The bsc uderlyg expermet s to defe coutble umber of detcl expermets. E : S, F, for,, Ech of these expermets wll be of the Beroull type wth detcl probbltes of success, d the result of the expermet, f performed, s depedet of prevous or future expermets. The ew expermet s descrbed s follows: erform expermet E f success s obted d t s the th, the stop; f ot, cotue to the ext expermet E. Cotue ths process utl the th success occurs. Let x be the umber of trls whch the th success occurs; the the probblty of tht evet c be show to be evet tht the th success occurs o the trl x x (.4) p p x,,, The followg exmple llustrtes the type of problems tht wll use the egtve Boml dstrbuto gve bove. xth Exmple: Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 4 of

15 Rdom Sgl rocessg Chpter Expermets d robblty Suppose the probblty of gettg f o the sgle toss of de s 0.. Fd () the probblty tht the fourth f occurs o the 6th trl; (b) tht the ffth f occurs before the 7th trl. Soluto: () (b) evet tht the 4th success occurs o the 6th trl evet tht the 5th success occurs before the 7th trl evet tht the 5th success occurs o the 5th trl evet tht the 5th success occurs o the 6th trl Selecto Combed Expermet A dfferet type of combed expermet wll ow be cosdered tht s combto of three or more expermets. For purpose of llustrto oly three compoet expermets re preseted. Let the three expermets be defed s E S, F,., E : S, F,., E : S, F,. 0 : The smple spce for E 0 cossts of oly two outcomes ( e d e ). These outcomes serve to help us select whch oe of the other expermets wll be performed. If the outcome s e, the E s performed wth outcome ; f the outcome s e the E s performed wth outcome. Thus the combto expermet. E : S, F, c be descrbed s follows: The smple spce S wll cosst of ordered prs of elemets the frst ether e or e d the secod ether S or S combed expermet results outcome e c e,, where e e,e d S S.. Thus trl of the The Borel feld F wll be defed to be the set of ll possble subsets of S, d the ssocted probblty mesure c be descrbed s e, e where j 0 j j f e e j j f e e The product of the probbltes s becuse of the ssumpto tht the expermet j or E 0 s depedet of (.7) Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 5 of

16 Rdom Sgl rocessg Chpter Expermets d robblty the expermets E d E..3 Codtol robblty The probblty mesure llows us to clculte the probbltes of ll evets tht re members of the Borel feld for the defed expermet. It wll become useful to defe the cocept of codtol probblty of evets. Gve codtog evet C such tht C 0 ssumg C s defed s C, the codtol probblty of y evet A p A C A C (.8) The followg exmples cosder the clculto of codtol probbltes for dscrete d cotuous smple spces. It s see tht the fudmetl set opertos d the probblty mesure for the uderlyg expermet re used to obt codtol probbltes. Exmple. Cosder expermet defed s sgle toss of crooed de wth smple spce S f, f f, f 3, f 4, f 5, 6. The probblty mesure, mybe bsed o pst hstory, s ow to be f f f f f f Let the codtog evet C be gve s C f, f f A C where Soluto 4 5 3, A f, f, the fce s less th or equl By the defto of codtol probblty, Eq. (.8), A C s AC f, ff, f3, f5 A C C f, f3, f5 f 6 f, f, f , the fce of the de s odd, d clculte the Exmple.3 Defe expermet tht hs s outcomes, t, the set of rel umbers greter th or equl to zero d tht the outcome represets the tme utl cert devce fls. The probblty mesure for the expermet s descrbed s Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 6 of

17 Rdom Sgl rocessg Chpter Expermets d robblty x flure tme t xe for x 0 The Borel feld s gve by the smllest feld cotg the tervls t : 0 t x for ll x 0. Fd the flure tme t flure tme t. Soluto From the defto of codtol probblty the probblty tht the tme to flure s less th or equl for the devce gve tht the flure tme s greter th s Usg set operto gves flure t flure t Ad the deomtor s see to be flure t flure t flure t flure t flure t flure t flure t flure t e e Substtutg these two results to the frst equto gves us the followg result flure t flure t flure t flure t e e e e.3. Totl robblty Theorem Gve evets A, A,, A such tht A for ll j A j (mutully exclusve) A S (exhustve) (.3) The t c be show tht the totl probblty of rbtrry evet B c be wrtte terms of the followg codtol probbltes s B B A A B A A B A A B A A (.3) Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 7 of

18 Rdom Sgl rocessg Chpter Expermets d robblty.3. Byes's Theorem A very mportt theorem tht hs my pplctos s Byes's theorem whch volves the determto of codtol probbltes A B A B p uder the sme frmewor s bove. It s esly show tht A B B B A A B A A B A A B A A B A A B A A (.33) The commucto recever d rdr sgl detector re the typcl exmples tht re represettve of the type of problem tht c be solved usg Byes's theorem d the totl probblty theorem. Exmple.4 Cosder expermet volvg rdom selecto of oe of three boxes. The rdom selecto s of sgle bll from the box chose. The boxes cot red, whte, d blue blls wth specfed probbltes of selecto. Assume tht 0.5 box 0.3 box3 0. box 0.4 red box 0.5 red box3 0. whte box 0.3 whte box 0.3 whte box3 box 0.3 blue box 0. blue box box red blue () Fd the probblty of gettg red bll. (b) Fd the codtol probblty, A B the evet red bll s selected. Soluto 0., where B s the evet box selected d A s () The totl probblty theorem c be used to obt the probblty of gettg red bll s follows: red box box red boxbox red box3box3 red (b) The probblty of box gve tht the bll s red c be determed by usg Byes s theorem d the results of prt () s box red boxbox red red Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 8 of

19 Rdom Sgl rocessg Chpter Expermets d robblty.4 Rdom ots The rdom plcemet of pots tervl s mportt problem. Coceptully t s logous to rdom rrvl tmes used bsc vetory problems, d t s used s bss for shot ose commucto theory. The rdom plcemet of the pots c be uformly or ouformly dstrbuted o tervl s see the followg sectos..4. Uform Rdom ots Itervl Defe expermet E : S, F,. s the rdom plcemet of pot t somewhere the closed tervl 0,T s show Fgure.3(). The smple spce s S t : 0 t T, F s the smllest feld cotg the sets t : t t for ll t S, d. s defed by t : t t t / T, for ll t S. Thus t c be see tht the probblty tht the pot selected wll be y gve tervl s the rto of tht tervl's legth to the totl legth T. robbltes of other evets tht re uos of ooverlppg tervls c be obted by ddg up the probbltes for ech of the tervls. 0 t T 0 T t t t - t () Smple spce (b) Rdom plcemet of pots pots 0 t t T t (c) pots tervl t out of pots The purpose of ths secto s to tl bout the rdom plcemet of pots, ot just oe pot, tervl 0,T see Fgure.3(b). A coveet wy to desg such expermet s to form ew compoud expermet E S, F,. composed of ordered -tuples of tmes, t, t,, : t, obted from repetg the expermet E defed bove depedetly. Assume depedece of the trls so tht the probblty mesure c be descrbed s the product Fgure.3 Rdom tmes tervl [0, T] t x t x, t x t x t x t x,, (.34) Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 9 of

20 Rdom Sgl rocessg Chpter Expermets d robblty The Borel feld s defed to be the smllest feld cotg the evets t x, t x,, t x for ll x, x,, x [0, ]. We re terested swerg questos reltg to clcultg the T probbltes tht cert umber of pots fll gve tervl or tervls. Sy tht the probblty tht exctly of the pots fll gve tervl t,t of legth t s show Fgure.3(c) s desred. Let A be the evet tht o sgle trl the pot selected t rdom flls the gve tervl. Usg the uform probblty mesure A t t t., we gve the probblty of A by Thus, o depedet trls, the probblty of gettg pots the tervl s bomlly dstrbuted s po t, t for trls p p t t where p (.35) T Further ssume tht, the umber of trls, s very lrge,, d tht the reltve wdth of the tervl s very smll, t / T, d s of the order t / T, The resultg osso pproxmto s wrtte s exctly out of t, t t t / T pot exp trls T! (.36) where t t t I the lmtg cse ths result wll gve terpretto terms of verge umber of pots per ut tervl. If, T, d / T, the t c be show tht exctly pot t lm (.37) t 0 t Aother probblty of terest s tht of gettg exctly pots tervl t d exctly b tervl t b s dcted Fgure.4. pots b pots 0 T t t b Fgure.4 Exctly pots tervl t d exctly b tervl tb Let A, B, C equl the evets tht o sgle trl of the expermet exctly oe pot flls t, t b, d ot t or t b, respectvely. The the probblty of gettg exctly exctly b pots tervl t b, d exctly b pots ot pots tervl t, t or t b out of trls c Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge 0 of

21 Rdom Sgl rocessg Chpter Expermets d robblty be obted from the multoml result s b t b tb t or! b t!! b b t! T b tb T t T tb T b (.38) The dvdul evets exctly pots t d exctly b pots t b out of trls hve probbltes s follows: t t t T tb T b t T tb T b (.39) Sce the product of the bove two probbltes does ot equl tht of Eq. (.38), the evets exctly pots t d exctly b pots t b out of trls re ot depedet evets..4. Nouform Rdom ots Itervl I cert problems the rdom pots re ot plced uformly the tervl. A commo wy to descrbe ouform rte s to ssg the probblty mesure by usg weghtg fucto the followg propertes: t 0 T 0 for t ll dt t [0, T ] t tht stsfes (.40) O sgle trl of the expermet, the rdom plcemet of sgle pot the tervl 0,T, the probblty tht the pot selected s t s gve by Thus, f t,t t t t, t ] t ( dt (.4) t hs pe t t t, t mes tht the pot selected t rdom hs hgher probblty of beg close to t th other vlues of t. If depedet trls of ths expermet re performed, the probblty of exctly pots out of trls beg the tervl t c be determed from Eq. (.6) s pot t, t p out of trls where p t t ( t, t ] t t,t p dt (.4) For the cse of ouform rte wth the ssumpto tht, the umber of trls, s very lrge,, Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge of

22 Rdom Sgl rocessg Chpter Expermets d robblty d tht the reltve wdth of the tervl t,t t s very smll, t / T, d of the order t / T, the osso pproxmto results the followg probblty where p s gve s bove: exctly out pot t, t of trls exp p p! (.43).5 Summry I ths chpter the mthemtcl deftos of expermet terms of smple spce, Borel feld, d probblty mesure were gve. The cocept of expermet s bsc to the uderstdg of rdom vrbles d rdom processes to be dscussed lter chpters. The ssgmet of the probblty mesure for severl expermets obted by combg other expermets led to the boml, multol, d hypergeometrc dstrbutos. The mportt cocept of codtol probblty, the totl probblty theorem d the Byes theorem re defed. Usg ths defto of the totl probblty theorem d the Byes theorem for obtg the posteror probblty followed. These cocepts hve fudmetl role the detecto d estmto of rdom vrbles d rdom processes s wll be see Chpters, 8, d 9. The chpter cocluded wth short dscusso o the rdom plcemet of pots gve tervl. These expermets re mportt lyzg problems volvg rdom tmes of rrvl d other relted problems. It ws ot the tet of ths chpter to gve exhustve presetto o expermets d ther use but to provde bcgroud mterl tht would be used the remder of the clss. For those wshg more thorough presetto there re my excellet texts, such s the boo, <robblty, Rdom Vrbles, d Stochstc rocesses>(pouls, Athsos, McGrw Hll, 965) Ths s the ed of Chpter Xd Uversty Lu Cogfeg E-Ml:cflu@ml.xd.edu.c ge of

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Wter 3 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is

In Calculus I you learned an approximation method using a Riemann sum. Recall that the Riemann sum is Mth Sprg 08 L Approxmtg Dete Itegrls I Itroducto We hve studed severl methods tht llow us to d the exct vlues o dete tegrls However, there re some cses whch t s ot possle to evlute dete tegrl exctly I

More information

Sequences and summations

Sequences and summations Lecture 0 Sequeces d summtos Istructor: Kgl Km CSE) E-ml: kkm0@kokuk.c.kr Tel. : 0-0-9 Room : New Mleum Bldg. 0 Lb : New Egeerg Bldg. 0 All sldes re bsed o CS Dscrete Mthemtcs for Computer Scece course

More information

MTH 146 Class 7 Notes

MTH 146 Class 7 Notes 7.7- Approxmte Itegrto Motvto: MTH 46 Clss 7 Notes I secto 7.5 we lered tht some defte tegrls, lke x e dx, cot e wrtte terms of elemetry fuctos. So, good questo to sk would e: How c oe clculte somethg

More information

Available online through

Available online through Avlble ole through wwwmfo FIXED POINTS FOR NON-SELF MAPPINGS ON CONEX ECTOR METRIC SPACES Susht Kumr Moht* Deprtmet of Mthemtcs West Begl Stte Uverst Brst 4 PrgsNorth) Kolt 76 West Begl Id E-ml: smwbes@yhoo

More information

Chapter 7. Bounds for weighted sums of Random Variables

Chapter 7. Bounds for weighted sums of Random Variables Chpter 7. Bouds for weghted sums of Rdom Vrbles 7. Itroducto Let d 2 be two depedet rdom vrbles hvg commo dstrbuto fucto. Htczeko (998 d Hu d L (2000 vestgted the Rylegh dstrbuto d obted some results bout

More information

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini

DATA FITTING. Intensive Computation 2013/2014. Annalisa Massini DATA FITTING Itesve Computto 3/4 Als Mss Dt fttg Dt fttg cocers the problem of fttg dscrete dt to obt termedte estmtes. There re two geerl pproches two curve fttg: Iterpolto Dt s ver precse. The strteg

More information

Roberto s Notes on Integral Calculus Chapter 4: Definite integrals and the FTC Section 2. Riemann sums

Roberto s Notes on Integral Calculus Chapter 4: Definite integrals and the FTC Section 2. Riemann sums Roerto s Notes o Itegrl Clculus Chpter 4: Defte tegrls d the FTC Secto 2 Rem sums Wht you eed to kow lredy: The defto of re for rectgle. Rememer tht our curret prolem s how to compute the re of ple rego

More information

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares

A Technique for Constructing Odd-order Magic Squares Using Basic Latin Squares Itertol Jourl of Scetfc d Reserch Publctos, Volume, Issue, My 0 ISSN 0- A Techque for Costructg Odd-order Mgc Squres Usg Bsc Lt Squres Tomb I. Deprtmet of Mthemtcs, Mpur Uversty, Imphl, Mpur (INDIA) tombrom@gml.com

More information

Chapter 3 Supplemental Text Material

Chapter 3 Supplemental Text Material S3-. The Defto of Fctor Effects Chpter 3 Supplemetl Text Mterl As oted Sectos 3- d 3-3, there re two wys to wrte the model for sglefctor expermet, the mes model d the effects model. We wll geerlly use

More information

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ]

Area and the Definite Integral. Area under Curve. The Partition. y f (x) We want to find the area under f (x) on [ a, b ] Are d the Defte Itegrl 1 Are uder Curve We wt to fd the re uder f (x) o [, ] y f (x) x The Prtto We eg y prttog the tervl [, ] to smller su-tervls x 0 x 1 x x - x -1 x 1 The Bsc Ide We the crete rectgles

More information

14.2 Line Integrals. determines a partition P of the curve by points Pi ( xi, y

14.2 Line Integrals. determines a partition P of the curve by points Pi ( xi, y 4. Le Itegrls I ths secto we defe tegrl tht s smlr to sgle tegrl except tht sted of tegrtg over tervl [ ] we tegrte over curve. Such tegrls re clled le tegrls lthough curve tegrls would e etter termology.

More information

Chapter 2 Intro to Math Techniques for Quantum Mechanics

Chapter 2 Intro to Math Techniques for Quantum Mechanics Fll 4 Chem 356: Itroductory Qutum Mechcs Chpter Itro to Mth Techques for Qutum Mechcs... Itro to dfferetl equtos... Boudry Codtos... 5 Prtl dfferetl equtos d seprto of vrbles... 5 Itroducto to Sttstcs...

More information

ICS141: Discrete Mathematics for Computer Science I

ICS141: Discrete Mathematics for Computer Science I Uversty o Hw ICS: Dscrete Mthemtcs or Computer Scece I Dept. Iormto & Computer Sc., Uversty o Hw J Stelovsy bsed o sldes by Dr. Be d Dr. Stll Orgls by Dr. M. P. Fr d Dr. J.L. Gross Provded by McGrw-Hll

More information

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ]

this is the indefinite integral Since integration is the reverse of differentiation we can check the previous by [ ] Atervtves The Itegrl Atervtves Ojectve: Use efte tegrl otto for tervtves. Use sc tegrto rules to f tervtves. Aother mportt questo clculus s gve ervtve f the fucto tht t cme from. Ths s the process kow

More information

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11:

Soo King Lim Figure 1: Figure 2: Figure 3: Figure 4: Figure 5: Figure 6: Figure 7: Figure 8: Figure 9: Figure 10: Figure 11: Soo Kg Lm 1.0 Nested Fctorl Desg... 1.1 Two-Fctor Nested Desg... 1.1.1 Alss of Vrce... Exmple 1... 5 1.1. Stggered Nested Desg for Equlzg Degree of Freedom... 7 1.1. Three-Fctor Nested Desg... 8 1.1..1

More information

SUM PROPERTIES FOR THE K-LUCAS NUMBERS WITH ARITHMETIC INDEXES

SUM PROPERTIES FOR THE K-LUCAS NUMBERS WITH ARITHMETIC INDEXES Avlble ole t http://sc.org J. Mth. Comput. Sc. 4 (04) No. 05-7 ISSN: 97-507 SUM PROPERTIES OR THE K-UCAS NUMBERS WITH ARITHMETIC INDEXES BIJENDRA SINGH POOJA BHADOURIA AND OMPRAKASH SIKHWA * School of

More information

Z = = = = X np n. n n. npq. npq pq

Z = = = = X np n. n n. npq. npq pq Stt 4, secto 4 Goodess of Ft Ctegory Probbltes Specfed otes by Tm Plchowsk Recll bck to Lectures 6c, 84 (83 the 8 th edto d 94 whe we delt wth populto proportos Vocbulry from 6c: The pot estmte for populto

More information

Analytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases

Analytical Approach for the Solution of Thermodynamic Identities with Relativistic General Equation of State in a Mixture of Gases Itertol Jourl of Advced Reserch Physcl Scece (IJARPS) Volume, Issue 5, September 204, PP 6-0 ISSN 2349-7874 (Prt) & ISSN 2349-7882 (Ole) www.rcourls.org Alytcl Approch for the Soluto of Thermodymc Idettes

More information

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS

PubH 7405: REGRESSION ANALYSIS REGRESSION IN MATRIX TERMS PubH 745: REGRESSION ANALSIS REGRESSION IN MATRIX TERMS A mtr s dspl of umbers or umercl quttes ld out rectgulr rr of rows d colums. The rr, or two-w tble of umbers, could be rectgulr or squre could be

More information

The z-transform. LTI System description. Prof. Siripong Potisuk

The z-transform. LTI System description. Prof. Siripong Potisuk The -Trsform Prof. Srpog Potsuk LTI System descrpto Prevous bss fucto: ut smple or DT mpulse The put sequece s represeted s ler combto of shfted DT mpulses. The respose s gve by covoluto sum of the put

More information

Chapter Unary Matrix Operations

Chapter Unary Matrix Operations Chpter 04.04 Ury trx Opertos After redg ths chpter, you should be ble to:. kow wht ury opertos mes,. fd the trspose of squre mtrx d t s reltoshp to symmetrc mtrces,. fd the trce of mtrx, d 4. fd the ermt

More information

MATH2999 Directed Studies in Mathematics Matrix Theory and Its Applications

MATH2999 Directed Studies in Mathematics Matrix Theory and Its Applications MATH999 Drected Studes Mthemtcs Mtr Theory d Its Applctos Reserch Topc Sttory Probblty Vector of Hgher-order Mrkov Ch By Zhg Sho Supervsors: Prof. L Ch-Kwog d Dr. Ch Jor-Tg Cotets Abstrct. Itroducto: Bckgroud.

More information

Itō Calculus (An Abridged Overview)

Itō Calculus (An Abridged Overview) Itō Clculus (A Abrdged Overvew) Arturo Ferdez Uversty of Clfor, Berkeley Sttstcs 157: Topcs I Stochstc Processes Semr Aprl 14, 211 1 Itroducto I my prevous set of otes, I troduced the cocept of Stochstc

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER VI Statistical Analysis of Experimental Data Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca

More information

2. Independence and Bernoulli Trials

2. Independence and Bernoulli Trials . Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,

More information

under the curve in the first quadrant.

under the curve in the first quadrant. NOTES 5: INTEGRALS Nme: Dte: Perod: LESSON 5. AREAS AND DISTANCES Are uder the curve Are uder f( ), ove the -s, o the dom., Prctce Prolems:. f ( ). Fd the re uder the fucto, ove the - s, etwee,.. f ( )

More information

Linear Algebra Concepts

Linear Algebra Concepts Ler Algebr Cocepts Ke Kreutz-Delgdo (Nuo Vscocelos) ECE 75A Wter 22 UCSD Vector spces Defto: vector spce s set H where ddto d sclr multplcto re defed d stsf: ) +( + ) (+ )+ 5) l H 2) + + H 6) 3) H, + 7)

More information

DISCRETE TIME MODELS OF FORWARD CONTRACTS INSURANCE

DISCRETE TIME MODELS OF FORWARD CONTRACTS INSURANCE G Tstsshvl DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE (Vol) 008 September DSCRETE TME MODELS OF FORWARD CONTRACTS NSURANCE GSh Tstsshvl e-ml: gurm@mdvoru 69004 Vldvosto Rdo str 7 sttute for Appled

More information

COMPLEX NUMBERS AND DE MOIVRE S THEOREM

COMPLEX NUMBERS AND DE MOIVRE S THEOREM COMPLEX NUMBERS AND DE MOIVRE S THEOREM OBJECTIVE PROBLEMS. s equl to b d. 9 9 b 9 9 d. The mgr prt of s 5 5 b 5. If m, the the lest tegrl vlue of m s b 8 5. The vlue of 5... s f s eve, f s odd b f s eve,

More information

Lecture 1. (Part II) The number of ways of partitioning n distinct objects into k distinct groups containing n 1,

Lecture 1. (Part II) The number of ways of partitioning n distinct objects into k distinct groups containing n 1, Lecture (Part II) Materals Covered Ths Lecture: Chapter 2 (2.6 --- 2.0) The umber of ways of parttog dstct obects to dstct groups cotag, 2,, obects, respectvely, where each obect appears exactly oe group

More information

A Brief Introduction to Olympiad Inequalities

A Brief Introduction to Olympiad Inequalities Ev Che Aprl 0, 04 The gol of ths documet s to provde eser troducto to olympd equltes th the stdrd exposto Olympd Iequltes, by Thoms Mldorf I ws motvted to wrte t by feelg gulty for gettg free 7 s o problems

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 Mtr Trsformtos usg Egevectors September 8, Mtr Trsformtos Usg Egevectors Lrry Cretto Mechcl Egeerg A Semr Egeerg Alyss September 8, Outle Revew lst lecture Trsformtos wth mtr of egevectors: = - A ermt

More information

Union, Intersection, Product and Direct Product of Prime Ideals

Union, Intersection, Product and Direct Product of Prime Ideals Globl Jourl of Pure d Appled Mthemtcs. ISSN 0973-1768 Volume 11, Number 3 (2015), pp. 1663-1667 Reserch Id Publctos http://www.rpublcto.com Uo, Itersecto, Product d Drect Product of Prme Idels Bdu.P (1),

More information

Random variables and sampling theory

Random variables and sampling theory Revew Rdom vrbles d smplg theory [Note: Beg your study of ths chpter by redg the Overvew secto below. The red the correspodg chpter the textbook, vew the correspodg sldeshows o the webste, d do the strred

More information

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION

St John s College. UPPER V Mathematics: Paper 1 Learning Outcome 1 and 2. Examiner: GE Marks: 150 Moderator: BT / SLS INSTRUCTIONS AND INFORMATION St Joh s College UPPER V Mthemtcs: Pper Lerg Outcome d ugust 00 Tme: 3 hours Emer: GE Mrks: 50 Modertor: BT / SLS INSTRUCTIONS ND INFORMTION Red the followg structos crefull. Ths questo pper cossts of

More information

Advanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University

Advanced Algorithmic Problem Solving Le 3 Arithmetic. Fredrik Heintz Dept of Computer and Information Science Linköping University Advced Algorthmc Prolem Solvg Le Arthmetc Fredrk Hetz Dept of Computer d Iformto Scece Lköpg Uversty Overvew Arthmetc Iteger multplcto Krtsu s lgorthm Multplcto of polyomls Fst Fourer Trsform Systems of

More information

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b

Discrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package

More information

Introduction to mathematical Statistics

Introduction to mathematical Statistics Itroducto to mthemtcl ttstcs Fl oluto. A grou of bbes ll of whom weghed romtely the sme t brth re rdomly dvded to two grous. The bbes smle were fed formul A; those smle were fed formul B. The weght gs

More information

CURVE FITTING LEAST SQUARES METHOD

CURVE FITTING LEAST SQUARES METHOD Nuercl Alss for Egeers Ger Jord Uverst CURVE FITTING Although, the for of fucto represetg phscl sste s kow, the fucto tself ot be kow. Therefore, t s frequetl desred to ft curve to set of dt pots the ssued

More information

ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS

ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl Alyss for Egeers Germ Jord Uversty ITERATIVE METHODS FOR SOLVING SYSTEMS OF LINEAR ALGEBRAIC EQUATIONS Numercl soluto of lrge systems of ler lgerc equtos usg drect methods such s Mtr Iverse, Guss

More information

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n

10.2 Series. , we get. which is called an infinite series ( or just a series) and is denoted, for short, by the symbol. i i n 0. Sere I th ecto, we wll troduce ere tht wll be dcug for the ret of th chpter. Wht ere? If we dd ll term of equece, we get whch clled fte ere ( or jut ere) d deoted, for hort, by the ymbol or Doe t mke

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Lecture 3 Probability review (cont d)

Lecture 3 Probability review (cont d) STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto

More information

Linear Algebra Concepts

Linear Algebra Concepts Ler Algebr Cocepts Nuo Vscocelos (Ke Kreutz-Delgdo) UCSD Vector spces Defto: vector spce s set H where ddto d sclr multplcto re defed d stsf: ) +( + ) = (+ )+ 5) H 2) + = + H 6) = 3) H, + = 7) ( ) = (

More information

Preliminary Examinations: Upper V Mathematics Paper 1

Preliminary Examinations: Upper V Mathematics Paper 1 relmr Emtos: Upper V Mthemtcs per Jul 03 Emer: G Evs Tme: 3 hrs Modertor: D Grgortos Mrks: 50 INSTRUCTIONS ND INFORMTION Ths questo pper sts of 0 pges, cludg swer Sheet pge 8 d Iformto Sheet pges 9 d 0

More information

The definite Riemann integral

The definite Riemann integral Roberto s Notes o Itegrl Clculus Chpter 4: Defte tegrls d the FTC Secto 4 The defte Rem tegrl Wht you eed to kow lredy: How to ppromte the re uder curve by usg Rem sums. Wht you c ler here: How to use

More information

A Series Illustrating Innovative Forms of the Organization & Exposition of Mathematics by Walter Gottschalk

A Series Illustrating Innovative Forms of the Organization & Exposition of Mathematics by Walter Gottschalk The Sgm Summto Notto #8 of Gottschlk's Gestlts A Seres Illustrtg Iovtve Forms of the Orgzto & Exposto of Mthemtcs by Wlter Gottschlk Ifte Vsts Press PVD RI 00 GG8- (8) 00 Wlter Gottschlk 500 Agell St #44

More information

Section 7.2 Two-way ANOVA with random effect(s)

Section 7.2 Two-way ANOVA with random effect(s) Secto 7. Two-wy ANOVA wth rdom effect(s) 1 1. Model wth Two Rdom ffects The fctors hgher-wy ANOVAs c g e cosdered fxed or rdom depedg o the cotext of the study. or ech fctor: Are the levels of tht fctor

More information

MATRIX AND VECTOR NORMS

MATRIX AND VECTOR NORMS Numercl lyss for Egeers Germ Jord Uversty MTRIX ND VECTOR NORMS vector orm s mesure of the mgtude of vector. Smlrly, mtr orm s mesure of the mgtude of mtr. For sgle comoet etty such s ordry umers, the

More information

1 Onto functions and bijections Applications to Counting

1 Onto functions and bijections Applications to Counting 1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of

More information

Chapter 4: Distributions

Chapter 4: Distributions Chpter 4: Dstrbutos Prerequste: Chpter 4. The Algebr of Expecttos d Vrces I ths secto we wll mke use of the followg symbols: s rdom vrble b s rdom vrble c s costt vector md s costt mtrx, d F m s costt

More information

Chapter Gauss-Seidel Method

Chapter Gauss-Seidel Method Chpter 04.08 Guss-Sedel Method After redg ths hpter, you should be ble to:. solve set of equtos usg the Guss-Sedel method,. reogze the dvtges d ptflls of the Guss-Sedel method, d. determe uder wht odtos

More information

Chapter 9 Jordan Block Matrices

Chapter 9 Jordan Block Matrices Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.

More information

PTAS for Bin-Packing

PTAS for Bin-Packing CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,

More information

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions

12 Iterative Methods. Linear Systems: Gauss-Seidel Nonlinear Systems Case Study: Chemical Reactions HK Km Slghtly moded //9 /8/6 Frstly wrtte t Mrch 5 Itertve Methods er Systems: Guss-Sedel Noler Systems Cse Study: Chemcl Rectos Itertve or ppromte methods or systems o equtos cosst o guessg vlue d the

More information

Modeling uncertainty using probabilities

Modeling uncertainty using probabilities S 1571 Itroduto to I Leture 23 Modelg uertty usg probbltes Mlos Huskreht mlos@s.ptt.edu 5329 Seott Squre dmstrto Fl exm: Deember 11 2006 12:00-1:50pm 5129 Seott Squre Uertty To mke dgost feree possble

More information

Chapter Linear Regression

Chapter Linear Regression Chpte 6.3 Le Regesso Afte edg ths chpte, ou should be ble to. defe egesso,. use sevel mmzg of esdul cte to choose the ght cteo, 3. deve the costts of le egesso model bsed o lest sques method cteo,. use

More information

The Occupancy and Coupon Collector problems

The Occupancy and Coupon Collector problems Chapter 4 The Occupacy ad Coupo Collector problems By Sarel Har-Peled, Jauary 9, 08 4 Prelmares [ Defto 4 Varace ad Stadard Devato For a radom varable X, let V E [ X [ µ X deote the varace of X, where

More information

Density estimation II

Density estimation II CS 750 Mche Lerg Lecture 6 esty estmto II Mlos Husrecht mlos@tt.edu 539 Seott Squre t: esty estmto {.. } vector of ttrute vlues Ojectve: estmte the model of the uderlyg rolty dstruto over vrles X X usg

More information

1. Overview of basic probability

1. Overview of basic probability 13.42 Desg Prcples for Ocea Vehcles Prof. A.H. Techet Sprg 2005 1. Overvew of basc probablty Emprcally, probablty ca be defed as the umber of favorable outcomes dvded by the total umber of outcomes, other

More information

The Mathematical Appendix

The Mathematical Appendix The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.

More information

On Solution of Min-Max Composition Fuzzy Relational Equation

On Solution of Min-Max Composition Fuzzy Relational Equation U-Sl Scece Jourl Vol.4()7 O Soluto of M-Mx Coposto Fuzzy eltol Equto N.M. N* Dte of cceptce /5/7 Abstrct I ths pper, M-Mx coposto fuzzy relto equto re studed. hs study s geerlzto of the works of Ohsto

More information

Summary of the lecture in Biostatistics

Summary of the lecture in Biostatistics Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the

More information

Linear Open Loop Systems

Linear Open Loop Systems Colordo School of Me CHEN43 Trfer Fucto Ler Ope Loop Sytem Ler Ope Loop Sytem... Trfer Fucto for Smple Proce... Exmple Trfer Fucto Mercury Thermometer... 2 Derblty of Devto Vrble... 3 Trfer Fucto for Proce

More information

(b) By independence, the probability that the string 1011 is received correctly is

(b) By independence, the probability that the string 1011 is received correctly is Soluto to Problem 1.31. (a) Let A be the evet that a 0 s trasmtted. Usg the total probablty theorem, the desred probablty s P(A)(1 ɛ ( 0)+ 1 P(A) ) (1 ɛ 1)=p(1 ɛ 0)+(1 p)(1 ɛ 1). (b) By depedece, the probablty

More information

Rendering Equation. Linear equation Spatial homogeneous Both ray tracing and radiosity can be considered special case of this general eq.

Rendering Equation. Linear equation Spatial homogeneous Both ray tracing and radiosity can be considered special case of this general eq. Rederg quto Ler equto Sptl homogeeous oth ry trcg d rdosty c be cosdered specl cse of ths geerl eq. Relty ctul photogrph Rdosty Mus Rdosty Rederg quls the dfferece or error mge http://www.grphcs.corell.edu/ole/box/compre.html

More information

i+1 by A and imposes Ax

i+1 by A and imposes Ax MASSACHUSETTS INSTITUTE OF TECHNOLOGY DEPARTMENT OF MECHANICAL ENGINEERING CAMBRIDGE, MASSACHUSETTS 09.9 NUMERICAL FLUID MECHANICS FALL 009 Mody, October 9, 009 QUIZ : SOLUTIONS Notes: ) Multple solutos

More information

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek

Optimality of Strategies for Collapsing Expanded Random Variables In a Simple Random Sample Ed Stanek Optmlt of Strteges for Collpsg Expe Rom Vrles Smple Rom Smple E Stek troucto We revew the propertes of prectors of ler comtos of rom vrles se o rom vrles su-spce of the orgl rom vrles prtculr, we ttempt

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution: Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed

More information

Differential Entropy 吳家麟教授

Differential Entropy 吳家麟教授 Deretl Etropy 吳家麟教授 Deto Let be rdom vrble wt cumultve dstrbuto ucto I F s cotuous te r.v. s sd to be cotuous. Let = F we te dervtve s deed. I te s clled te pd or. Te set were > 0 s clled te support set

More information

,... are the terms of the sequence. If the domain consists of the first n positive integers only, the sequence is a finite sequence.

,... are the terms of the sequence. If the domain consists of the first n positive integers only, the sequence is a finite sequence. Chpter 9 & 0 FITZGERALD MAT 50/5 SECTION 9. Sequece Defiitio A ifiite sequece is fuctio whose domi is the set of positive itegers. The fuctio vlues,,, 4,...,,... re the terms of the sequece. If the domi

More information

Metric Spaces: Basic Properties and Examples

Metric Spaces: Basic Properties and Examples 1 Metrc Spces: Bsc Propertes d Exmples 1.1 NTODUCTON Metrc spce s dspesble termedte course of evoluto of the geerl topologcl spces. Metrc spces re geerlstos of Euclde spce wth ts vector spce structure

More information

Centroids & Moments of Inertia of Beam Sections

Centroids & Moments of Inertia of Beam Sections RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol

More information

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.

means the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever. 9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc,

More information

Regression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers

Regression. By Jugal Kalita Based on Chapter 17 of Chapra and Canale, Numerical Methods for Engineers Regresso By Jugl Klt Bsed o Chpter 7 of Chpr d Cle, Numercl Methods for Egeers Regresso Descrbes techques to ft curves (curve fttg) to dscrete dt to obt termedte estmtes. There re two geerl pproches two

More information

Mathematics HL and further mathematics HL formula booklet

Mathematics HL and further mathematics HL formula booklet Dplom Progrmme Mthemtcs HL d further mthemtcs HL formul boolet For use durg the course d the emtos Frst emtos 04 Publshed Jue 0 Itertol Bcclurete Orgzto 0 5048 Mthemtcs HL d further mthemtcs formul boolet

More information

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen.

2.28 The Wall Street Journal is probably referring to the average number of cubes used per glass measured for some population that they have chosen. .5 x 54.5 a. x 7. 786 7 b. The raked observatos are: 7.4, 7.5, 7.7, 7.8, 7.9, 8.0, 8.. Sce the sample sze 7 s odd, the meda s the (+)/ 4 th raked observato, or meda 7.8 c. The cosumer would more lkely

More information

Functions of Random Variables

Functions of Random Variables Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,

More information

CHAPTER 4 RADICAL EXPRESSIONS

CHAPTER 4 RADICAL EXPRESSIONS 6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

On Several Inequalities Deduced Using a Power Series Approach

On Several Inequalities Deduced Using a Power Series Approach It J Cotemp Mth Sceces, Vol 8, 203, o 8, 855-864 HIKARI Ltd, wwwm-hrcom http://dxdoorg/02988/jcms2033896 O Severl Iequltes Deduced Usg Power Seres Approch Lored Curdru Deprtmet of Mthemtcs Poltehc Uversty

More information

POWERS OF COMPLEX PERSYMMETRIC ANTI-TRIDIAGONAL MATRICES WITH CONSTANT ANTI-DIAGONALS

POWERS OF COMPLEX PERSYMMETRIC ANTI-TRIDIAGONAL MATRICES WITH CONSTANT ANTI-DIAGONALS IRRS 9 y 04 wwwrppresscom/volumes/vol9issue/irrs_9 05pdf OWERS OF COLE ERSERIC I-RIIGOL RICES WIH COS I-IGOLS Wg usu * Q e Wg Hbo & ue College of Scece versty of Shgh for Scece d echology Shgh Ch 00093

More information

On a class of analytic functions defined by Ruscheweyh derivative

On a class of analytic functions defined by Ruscheweyh derivative Lfe Scece Jourl ;9( http://wwwlfescecestecom O clss of lytc fuctos defed by Ruscheweyh dervtve S N Ml M Arf K I Noor 3 d M Rz Deprtmet of Mthemtcs GC Uversty Fslbd Pujb Pst Deprtmet of Mthemtcs Abdul Wl

More information

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg

More information

Bond Additive Modeling 5. Mathematical Properties of the Variable Sum Exdeg Index

Bond Additive Modeling 5. Mathematical Properties of the Variable Sum Exdeg Index CROATICA CHEMICA ACTA CCACAA ISSN 00-6 e-issn -7X Crot. Chem. Act 8 () (0) 9 0. CCA-5 Orgl Scetfc Artcle Bod Addtve Modelg 5. Mthemtcl Propertes of the Vrble Sum Edeg Ide Dmr Vukčevć Fculty of Nturl Sceces

More information

Patterns of Continued Fractions with a Positive Integer as a Gap

Patterns of Continued Fractions with a Positive Integer as a Gap IOSR Jourl of Mthemtcs (IOSR-JM) e-issn: 78-578, -ISSN: 39-765X Volume, Issue 3 Ver III (My - Ju 6), PP -5 wwwosrjourlsorg Ptters of Cotued Frctos wth Postve Iteger s G A Gm, S Krth (Mthemtcs, Govermet

More information

Probabilistic approach to the distribution of primes and to the proof of Legendre and Elliott-Halberstam conjectures VICTOR VOLFSON

Probabilistic approach to the distribution of primes and to the proof of Legendre and Elliott-Halberstam conjectures VICTOR VOLFSON Probblstc pproch to the dstrbuto of prmes d to the proof of Legedre d Ellott-Hlberstm cojectures VICTOR VOLFSON ABSTRACT. Probblstc models for the dstrbuto of prmes the turl umbers re costructed the rtcle.

More information

Investigating Cellular Automata

Investigating Cellular Automata Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte

More information

Special Instructions / Useful Data

Special Instructions / Useful Data JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth

More information

Mu Sequences/Series Solutions National Convention 2014

Mu Sequences/Series Solutions National Convention 2014 Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

Introduction to Probability

Introduction to Probability Itroducto to Probablty Nader H Bshouty Departmet of Computer Scece Techo 32000 Israel e-mal: bshouty@cstechoacl 1 Combatorcs 11 Smple Rules I Combatorcs The rule of sum says that the umber of ways to choose

More information

Logistic regression (continued)

Logistic regression (continued) STAT562 page 138 Logstc regresso (cotued) Suppose we ow cosder more complex models to descrbe the relatoshp betwee a categorcal respose varable (Y) that takes o two (2) possble outcomes ad a set of p explaatory

More information

Lecture 07: Poles and Zeros

Lecture 07: Poles and Zeros Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto

More information

Application: Work. 8.1 What is Work?

Application: Work. 8.1 What is Work? Applcto: Work 81 Wht s Work? Work, the physcs sese, s usully defed s force ctg over dstce Work s sometmes force tmes dstce, 1 but ot lwys Work s more subtle th tht Every tme you exert force, t s ot the

More information

Econometric Methods. Review of Estimation

Econometric Methods. Review of Estimation Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators

More information

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015

Homework 1: Solutions Sid Banerjee Problem 1: (Practice with Asymptotic Notation) ORIE 4520: Stochastics at Scale Fall 2015 Fall 05 Homework : Solutos Problem : (Practce wth Asymptotc Notato) A essetal requremet for uderstadg scalg behavor s comfort wth asymptotc (or bg-o ) otato. I ths problem, you wll prove some basc facts

More information

Class 13,14 June 17, 19, 2015

Class 13,14 June 17, 19, 2015 Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral

More information

Integration by Parts for D K

Integration by Parts for D K Itertol OPEN ACCESS Jourl Of Moder Egeerg Reserc IJMER Itegrto y Prts for D K Itegrl T K Gr, S Ry 2 Deprtmet of Mtemtcs, Rgutpur College, Rgutpur-72333, Purul, West Begl, Id 2 Deprtmet of Mtemtcs, Ss Bv,

More information