Imperfect Information
|
|
- Leo Boyd
- 5 years ago
- Views:
Transcription
1 Imerfec Informaon Comlee Informaon - all layers know: Se of layers Se of sraeges for each layer Oucomes as a funcon of he sraeges Payoffs for each oucome (.e. uly funcon for each layer Incomlee Informaon - any of he four eces of nformaon above s unknown o a layer; also called asymmerc nformaon Prvae Informaon - ycal examle of ncomlee nformaon where layer knows somehng abou hs own acons ha oher layers don' observe; could be he oher way hough (e.g.ecals [docor mechanc] has more nformaon abou your ayoffs han you do Harsany - aer n Managemen Scence (967 on how o conver game wh ncomlee nformaon o game of merfec bu comlee nformaon by nserng random move by naure Incomlee n Payoffs - Harsany frs argued ha all yes of ncomlee nformaon can be convered o ncomlee nformaon on ayoffs Players - no sure f layer s n he game... equvalen o knowng he s n he game bu no sure abou hs sraegy sace (e.g. could have four oons or jus one effecvely no n he game because he has no decsons Player Sraeges Sraeges - no knowng se of sraeges could be ransformed by addng sraeges ha wll never be layed because of - ayoff; now all oons have same number of sraeges bu we don' know whch ayoffs are correc Sraeges Payoffs Oucomes - no knowng oucomes s equvalen o no knowng he uly funcons (ayoffs... echncally here s a dfference o knowng oucomes and no uly funcons f here are several sraeges (from dfferen nformaon ses ha have he same oucome (wll yeld he same ayoffs Infne Regress - n order o solve ncomlee nformaon roblem we have o worry abou belefs abou belefs abou belefs ec. Soluon - Harsany solved roblem by defnng yes of layers where layers dffer n ayoffs (uly funcon and/or belefs Smlfcaon - Harsany also argued ha all belefs could be caured by a sngle robably dsrbuon over he yes of he oher layers (could be correlaed by usng a jon dsrbuon Examle - 3 yes of "layer " and yes of "layer ": and of Each ye of "layer " has belefs abou he robably ha an oonen wll be of a secfc ye: = ( = ( = ( In or Ou? or or or
2 Fudge - n order o be racable hs mehod requres a fne number of yes Sandard - ycally we only allow layers yes o dffer on reference (uly funcon; heory allows references and/or belefs o vary bu he full mlcaons of hs haven' been suded Problem? - may have assumed away he roblem of ncomlee nformaon raher han solved Formally - Players - n layers Sraeges - each ye of layer has he same sraegy sace S (oal of n Se of Tyes - T s se of dfferen yes of layer (oal of n ses of yes Examle - from revous age T = { 3 } T = { 3 } Examle - yes of layer yes of layer and 3 yes of layer 3: T a b T c d T x y z = { } = { } 3 = { } Secfc Player - s he j h ye of layer (hs was denoed of 7 j on revous age Oher Player Tyes - T = T T T T + Tn... he Caresan roduc of he ses of all layer yes exce for layer T = ( a c( a d( b c( b Examle - { } Oher Players - 3 d T s a secfc ye of each layer (exce layer Examle - one of he elemens of T 3 above Belefs - = Pr[ ] = subjecve belef of j h ye of layer abou he robably ha hs secfc combnaon of oonens wll occur = se of all for layer Examle - 3x = ( y = ( z = ( So ye x of layer 3 hnks 's equally lkely o see any combnaon of yes of layers and ; ye y of layer 3 hnks he'll face ( a c (.e. ye a of layer and ye c of layer abou a ffh of he me; ec. = { } 3 3x 3 y 3z Preferences - u ( s ; uly for layer deends on he acual yes of each layer ( and he sraeges ha each layer uses (s Γ S S T T u u n n n n Sraeges Tyes Belefs Prefs Bayesan Game of Incomlee Informaon - ( (assume hs s common knowledge Examle of Belefs - n oker here are 5!/(7!5! ossble hands; ror o dealng all hands have equal robably; afer seeng your cards (and ohers avalable he robables assgned o dfferen yes of oonens (.e. hands hey have change... for examle f you have aces he robably ha an oonen has a hree or four of a knd wh aces or any oher hand ha requres more han aces wll be zero Professonal Poker - assume layer has queens and layer has jacks; on TV hey say n hs scenaro layer has a 90% chance of wnnng and layer only has a 0% chanceuggesng ha 's a bad dea for layer o say n he game... bu ha analyss s based on comlee nformaon; he layers have ncomlee nformaon so he relevan robables are each layer's (subjecve robably of wnnng gven hs
3 own cards (and hose he's seen; ha s each layer s basng hs decson on hs belef abou hs oonen's hand (usually a lo more robably on oor hands han good hands; f boh layers are sll n he game ha means hey boh hnk hey have greaer han 50% chance of wnnng Conssency - Pr[ ] = Pr[ ] = where Pr[ ] = Pr[ ] Pr[ ] Subjecve Objecve Examle - 3 layers each wh yes has oal of 8 combnaons (so each ye of each layer faces combnaons of ossble oonens: Pr[ 3] No Bg Deal - Almond showed nconssen belefs can be ransformed no conssen belefs; we usually jus assume belefs are conssen because of hs Max Execed Uly - max u ( s ( ; Pr[ ] s Suose belef = Pr[ ] s no conssen ( u ( s above s ncorrec Suose belef ˆ s conssen Always Conssen - le N ( T = # of vecors T = roduc of number of yes of all layers exce ; (examle above has N ( T 3 = Assume each s equally lkely so ˆ = Pr[ ] = / N( T Based on he defnon of conssency belef Defne new uly uˆ ( s N( T u ( s = Noe ha ˆ uˆ ( s = u ( s Pr[ 3] Pr[ 3] = Pr[ 3] + Pr[ 3 ] + Pr[ 3 ] + Pr[ 3 ] = 3 ˆ s conssen Tha means execed uly wh conssen and nconssen belefs s he same Cach - have o know he "werd" (nconssen robables o ncororae no references (new uly funcon 3 of 7
4 Equlbrum - wo deas on how o fnd Players Aroach - rea each ye of each layer as a searae layer (e.g. layers each wh yes becomes a layer game; "layers aroach" s Len's erm; Slusky called he ex os aroach (meanng layers choose her sraeges afer knowng her ye Game Srucure - no all layers wll face each oher (e.g. doesn' lay agans ; hs means he reacon funcon only deends on some of he oonens (n he examle would be oonens nsead of 3 Ineracon - (anoher Poker asde a layer of one ye may wan o affec ayoffs when he's anoher ye; Examles: Beng - be wh a bad hand so oonen knows you be wh a bad hand and hen doesn' know when you have a good hand Bluffng - f you bluff and he oonen folds and asks o see your cards here are wo oons: - Tell oonen ha o see he hand he needs o call (mach be... n oher words he has o ay for he nformaon - Show hm your hand so he knows you're bluffng; ha way f you be laer wh a good hand he mgh hnk you're bluffng and be agans you Problem - hese are use reeaed game reasonng (we're sll focusng on sngle-sho games σ ( s an equlbrum sraegy for (ye j of layer f and T T u (σ ( ; Pr[ ] u ( s ; Pr[ ] s S T Englsh - akng he oher layers' sraeges ( as gven layer 's bes rely s σ ( (.e. he execed ayoff s greaer han or equal o he execed ayoff of layng any oher sraegy s n hs sraegy sace S ; n order for hs o be an equlbrum all he layers mus be layng bes reles o her oonens' bes reles so hs condon holds for all yes of all layers ( and T Noe: alernave sraegy doesn' need o be ndexed by he layer ye ( j because we assumed ha all yes of he same layer have he same sraegy sace (o of. Sraeges Aroach - frs move n game s naure choosng layers yes; solve an merfec bu comlee nformaon game; "sraeges aroach" s Len's erm; Slusky called he ex ane aroach (layers choose sraeges before knowng her ye +: Fewer layers : More comlcaed sraeges (mus address sraeges for each layer ye Examle - 3 yes of layer ; yes of layer Naure 3 Dfferen? - many quesons on wheher hese wo mehods are dfferen n equlbra or mehod (e.g. easer o solve Subgame Perfecon - he layers aroach guaranees subgame erfec; some eole argue he sraeges aroach could have sraeges ha aren' subgame erfec Harsany - argued he wo were dfferen; dea of mmedae vs. delayed commmen (here "commmen" means ckng a sraegy Immedae Commmen - a sar of game before knowng ye (sraeges aroach of 7
5 Delayed Commmen - choose sraegy afer knowng ye (layers aroach Slusky - "The ermnology s no longer used n ar because 's no useful." Problem - Harsany argued hese are dfferen and sad delayed commmen s beer bu hs examle roblem combned cooerave and non-cooerave elemens Slusky - no sure wha equlbrum noon s n a mxed cooerave and noncooerave game Same - n fully non-cooerave game sraeges and layers aroach are THE SAME; some felds (e.g. regulaon use he layers aroach because "'s rgh" bu hey're ncorrecly aly Harsany concluson o fully non-cooerave games Hsory - Blackwell Nash Von-Neumann Kuhn all dd wo-layer oker examles usng he sraeges aroach 5 years before Harsany; hey usually used a smlfed verson lke layers each wh 3 yes (hgh medum and low hand; n ha caseraeges aroach s easer hen layers aroach ( layer 8 sraegy game vs. 6 layer sraegy Players Aroach Easer - for nfne yes wh connuous sraegy saces he "easy" way s o arameerze ye and solve usng he layers aroach (e.g. rncal agen roblem; consumer maxmzaon; frm rof maxmzaon Examle - smles case: layers yes of each sraeges: a a and b b Players Aroach - game shown here s Player for layer (only shows hs ayoffs b b even hough we need o know layer a x x 's o solve he game; a a mnmum we also need anoher ar of ayoff a x 3 x ables for layer ; ha wll conan all he nformaon alhough Slusky refers o have a ar for each layer ye (oal of four of hese Noaon: = Pr[ lays a ] (so = Pr[ lays a ] = Pr[ beleves s hs oonen] (so = Pr[ beleves s hs oonen] So he robables always defaul o he frs sraegy or he frs ye of he oonen Execed Uly - E[ u ] = x + ( x + ( x3 + ( ( x + ( y + ( ( y + ( ( y + 3 ( ( ( y Ths s lnear n o we can wre roblem as max f ( x - x y - y + c Prob n box (a b vs. Payoff n box (a b Player Prob n box (a b vs. Player Chosen by naure Payoff n box (a b Player b b a y y a y 3 y... only & are from oher layers Bes Rely - f f ( > 0 = 0 If f ( < 0 (0 f f ( = 0 There wll be a bes rely for he oher hree layer yes resulng n equaons wh unknowns ( 5 of 7
6 Sraeges Aroach - wll show hs s he same as he layers aroach; assume Eq s he ure sraegy equlbrum New Noaon - = Pr[ ] (robably layer s ye Same as Players Aroach - use execed ayoffs o show ex os ex ane Look a Eq frs: Player ye (rob s lays a ; hs oonen could be ye of layer (rob Pr[ ] and lays b or ye of layer (rob Pr[ ] and lays b Player ye (rob s lays a ; hs oonens are he same and lay he same sraeges (bu he condonal robables are dfferen E[Eq] = [ Pr[ ] u ( a b ; + Pr[ ] u ( a b ; + [ ] u ( a b ; + Pr[ ] u ( a b ; Pr[ Snce hs s an equlbrum we know: E[Eq] E[c] c has he followng sraeges ( a a bb so bascally only he sraegy for layer s changed (from a o a ; usng he same logc as above we have E[r] = [ Pr[ ] u ( a b ; + Pr[ ] u ( a b ; + [ ] u ( a b ; + Pr[ ] u ( a b ; Pr[ The corresondng crcled erms cancel ou so E[Eq] E[c] becomes [ Pr[ ] u ( a b ; + Pr[ ] u ( a b ; [ ] u ( a b ; + Pr[ ] u ( a b ; Pr[ Tha s he execed ayoff of a once layer knows he's ye s o he execed ayoff of a once he knows he's ye : E[ u ( a E[ u ( a We can use hs same logc o ge E[Eq] E[c] yelds same condon as layng a over a E[Eq] E[r] yelds same condon as layng b over b E[Eq] E[r] yelds same condon as layng b over b Combnng all four comarson yelds he same equlbrum sraegy as he ex os game: s = a = a = b = b equlbrum n ex ane game s he same equlbrum n he ex os game Now assume s = a = a = b = b s an equlbrum n he ex os game; we wan o show hs s also an equlbrum n he ex ane game; usng he work above we can work backwards o show Eq = ( aa bb has a hgher execed ayoff han r r c c; now consder E[Eq] vs. E[r];.e. comare ( s = a = a = b = b o ( s = a = a = b = b so boh yes of layer are changng her sraegy; changng he noaon a lle for convenence: E[Eq] = E u ( a + E[ u ( [ a E[ u ( a E[ u ( a E[r] = + Player b b b b b b b b a a c a a c a a r r Eq r a a Player 's sraegy f he's: Tye Tye Player c 6 of 7
7 From he equlbrum assumon we know E u ( a E[ u ( and [ a E[ u ( a E[ u ( a (.e. each erm n E[Eq] s 's resecve erm n E[r]... ha means E[Eq] E[r] We can reea hs logc o show E[Eq] E[c] ( s = a = a = b = b n he ex os game s he same as ( a a bb n he ex ane game Why? - hs works because he yes are ndeenden of each oher; we can' have and a he same me so here s no neracon (.e. can change boh her sraeges a he same me and 's equvalen o change one a a me Mxed Sraeges - frs look a ex os; suose lays (// and lays (/3/3 n he ex ane game ha's he same as: ex ane a a /3 /6 a a / /3 /6 a a / /3 /6 a a /3 /6 Problem? - gong he oher way 's ossble o come u wh a mxed sraegy n he ex ane game ha can' be relcaed by mxed sraeges n he ex os game (e.g. (0//0; hs s ar of he nuon why Harsany sad hey were dfferen No Problem - won' ge hs as a mxed sraegy because here's no gan n correlaon beween he ye yes; (0//0 mgh as well be (//// whch also has each ye layng a mxed sraegy; n a real layer game here could be correlaed sraeges so hs sn' he case 7 of 7
Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)
Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on
More informationSolution in semi infinite diffusion couples (error function analysis)
Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of
More informationPHYS 705: Classical Mechanics. Canonical Transformation
PHYS 705: Classcal Mechancs Canoncal Transformaon Canoncal Varables and Hamlonan Formalsm As we have seen, n he Hamlonan Formulaon of Mechancs,, are ndeenden varables n hase sace on eual foong The Hamlon
More informationAdvanced time-series analysis (University of Lund, Economic History Department)
Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng
More informationMechanics Physics 151
Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu
More information( ) () we define the interaction representation by the unitary transformation () = ()
Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger
More informationTSS = SST + SSE An orthogonal partition of the total SS
ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally
More informationMechanics Physics 151
Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm Hqp (,,) = qp Lqq (,,) H p = q H q = p H L = Equvalen o Lagrangan formalsm Smpler, bu wce as
More informationDensity Matrix Description of NMR BCMB/CHEM 8190
Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon Alernae approach o second order specra: ask abou x magnezaon nsead of energes and ranson probables. If we say wh one bass se, properes vary
More informationEP2200 Queuing theory and teletraffic systems. 3rd lecture Markov chains Birth-death process - Poisson process. Viktoria Fodor KTH EES
EP Queung heory and eleraffc sysems 3rd lecure Marov chans Brh-deah rocess - Posson rocess Vora Fodor KTH EES Oulne for oday Marov rocesses Connuous-me Marov-chans Grah and marx reresenaon Transen and
More informationGraduate Macroeconomics 2 Problem set 5. - Solutions
Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K
More informationIn the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!
ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal
More informationFI 3103 Quantum Physics
/9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon
More information. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.
Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons
More informationDensity Matrix Description of NMR BCMB/CHEM 8190
Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon If we say wh one bass se, properes vary only because of changes n he coeffcens weghng each bass se funcon x = h< Ix > - hs s how we calculae
More informationHow about the more general "linear" scalar functions of scalars (i.e., a 1st degree polynomial of the following form with a constant term )?
lmcd Lnear ransformaon of a vecor he deas presened here are que general hey go beyond he radonal mar-vecor ype seen n lnear algebra Furhermore, hey do no deal wh bass and are equally vald for any se of
More informationEpistemic Game Theory: Online Appendix
Epsemc Game Theory: Onlne Appendx Edde Dekel Lucano Pomao Marcano Snscalch July 18, 2014 Prelmnares Fx a fne ype srucure T I, S, T, β I and a probably µ S T. Le T µ I, S, T µ, βµ I be a ype srucure ha
More informationEndogeneity. Is the term given to the situation when one or more of the regressors in the model are correlated with the error term such that
s row Endogeney Is he erm gven o he suaon when one or more of he regressors n he model are correlaed wh he error erm such ha E( u 0 The 3 man causes of endogeney are: Measuremen error n he rgh hand sde
More informationUNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION
INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he
More information. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.
Mah E-b Lecure #0 Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons are
More informationLecture VI Regression
Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M
More informationLet s treat the problem of the response of a system to an applied external force. Again,
Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem
More informationLecture 6: Learning for Control (Generalised Linear Regression)
Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson
More informationEcon107 Applied Econometrics Topic 5: Specification: Choosing Independent Variables (Studenmund, Chapter 6)
Econ7 Appled Economercs Topc 5: Specfcaon: Choosng Independen Varables (Sudenmund, Chaper 6 Specfcaon errors ha we wll deal wh: wrong ndependen varable; wrong funconal form. Ths lecure deals wh wrong ndependen
More informationDynamic Team Decision Theory. EECS 558 Project Shrutivandana Sharma and David Shuman December 10, 2005
Dynamc Team Decson Theory EECS 558 Proec Shruvandana Sharma and Davd Shuman December 0, 005 Oulne Inroducon o Team Decson Theory Decomposon of he Dynamc Team Decson Problem Equvalence of Sac and Dynamc
More information[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5
TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres
More informationV.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS
R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon
More informationScattering at an Interface: Oblique Incidence
Course Insrucor Dr. Raymond C. Rumpf Offce: A 337 Phone: (915) 747 6958 E Mal: rcrumpf@uep.edu EE 4347 Appled Elecromagnecs Topc 3g Scaerng a an Inerface: Oblque Incdence Scaerng These Oblque noes may
More informationCS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4
CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped
More informationLinear Response Theory: The connection between QFT and experiments
Phys540.nb 39 3 Lnear Response Theory: The connecon beween QFT and expermens 3.1. Basc conceps and deas Q: ow do we measure he conducvy of a meal? A: we frs nroduce a weak elecrc feld E, and hen measure
More informationCS286.2 Lecture 14: Quantum de Finetti Theorems II
CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2
More informationPolitical Economy of Institutions and Development: Problem Set 2 Due Date: Thursday, March 15, 2019.
Polcal Economy of Insuons and Developmen: 14.773 Problem Se 2 Due Dae: Thursday, March 15, 2019. Please answer Quesons 1, 2 and 3. Queson 1 Consder an nfne-horzon dynamc game beween wo groups, an ele and
More informationTHEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that
THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because
More informationNPTEL Project. Econometric Modelling. Module23: Granger Causality Test. Lecture35: Granger Causality Test. Vinod Gupta School of Management
P age NPTEL Proec Economerc Modellng Vnod Gua School of Managemen Module23: Granger Causaly Tes Lecure35: Granger Causaly Tes Rudra P. Pradhan Vnod Gua School of Managemen Indan Insue of Technology Kharagur,
More informationVariants of Pegasos. December 11, 2009
Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on
More informationAdvanced Machine Learning & Perception
Advanced Machne Learnng & Percepon Insrucor: Tony Jebara SVM Feaure & Kernel Selecon SVM Eensons Feaure Selecon (Flerng and Wrappng) SVM Feaure Selecon SVM Kernel Selecon SVM Eensons Classfcaon Feaure/Kernel
More informationMechanics Physics 151
Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ
More informationCH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC
CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal
More informationOrdinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s
Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class
More information1 Review of Zero-Sum Games
COS 5: heoreical Machine Learning Lecurer: Rob Schapire Lecure #23 Scribe: Eugene Brevdo April 30, 2008 Review of Zero-Sum Games Las ime we inroduced a mahemaical model for wo player zero-sum games. Any
More informationNotes on the stability of dynamic systems and the use of Eigen Values.
Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon
More informationA New Generalized Gronwall-Bellman Type Inequality
22 Inernaonal Conference on Image, Vson and Comung (ICIVC 22) IPCSIT vol. 5 (22) (22) IACSIT Press, Sngaore DOI:.7763/IPCSIT.22.V5.46 A New Generalzed Gronwall-Bellman Tye Ineualy Qnghua Feng School of
More informationAdvanced Macroeconomics II: Exchange economy
Advanced Macroeconomcs II: Exchange economy Krzyszof Makarsk 1 Smple deermnsc dynamc model. 1.1 Inroducon Inroducon Smple deermnsc dynamc model. Defnons of equlbrum: Arrow-Debreu Sequenal Recursve Equvalence
More information( ) [ ] MAP Decision Rule
Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure
More informationOnline Appendix for. Strategic safety stocks in supply chains with evolving forecasts
Onlne Appendx for Sraegc safey socs n supply chans wh evolvng forecass Tor Schoenmeyr Sephen C. Graves Opsolar, Inc. 332 Hunwood Avenue Hayward, CA 94544 A. P. Sloan School of Managemen Massachuses Insue
More informationDepartment of Economics University of Toronto
Deparmen of Economcs Unversy of Torono ECO408F M.A. Economercs Lecure Noes on Heeroskedascy Heeroskedascy o Ths lecure nvolves lookng a modfcaons we need o make o deal wh he regresson model when some of
More informationPattern Classification (III) & Pattern Verification
Preare by Prof. Hu Jang CSE638 --4 CSE638 3. Seech & Language Processng o.5 Paern Classfcaon III & Paern Verfcaon Prof. Hu Jang Dearmen of Comuer Scence an Engneerng York Unversy Moel Parameer Esmaon Maxmum
More informationKeywords: Hedonic regressions; hedonic indexes; consumer price indexes; superlative indexes.
Hedonc Imuaon versus Tme Dummy Hedonc Indexes Erwn Dewer, Saeed Herav and Mck Slver December 5, 27 (wh a commenary by Jan de Haan) Dscusson Paer 7-7, Dearmen of Economcs, Unversy of Brsh Columba, 997-873
More informationMath 128b Project. Jude Yuen
Mah 8b Proec Jude Yuen . Inroducon Le { Z } be a sequence of observed ndependen vecor varables. If he elemens of Z have a on normal dsrbuon hen { Z } has a mean vecor Z and a varancecovarance marx z. Geomercally
More informationFTCS Solution to the Heat Equation
FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence
More information1 Constant Real Rate C 1
Consan Real Rae. Real Rae of Inees Suppose you ae equally happy wh uns of he consumpon good oday o 5 uns of he consumpon good n peod s me. C 5 Tha means you ll be pepaed o gve up uns oday n eun fo 5 uns
More information(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function
MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,
More informationComputing Relevance, Similarity: The Vector Space Model
Compung Relevance, Smlary: The Vecor Space Model Based on Larson and Hears s sldes a UC-Bereley hp://.sms.bereley.edu/courses/s0/f00/ aabase Managemen Sysems, R. Ramarshnan ocumen Vecors v ocumens are
More informationChapter Lagrangian Interpolation
Chaper 5.4 agrangan Inerpolaon Afer readng hs chaper you should be able o:. dere agrangan mehod of nerpolaon. sole problems usng agrangan mehod of nerpolaon and. use agrangan nerpolans o fnd deraes and
More informationECON 8105 FALL 2017 ANSWERS TO MIDTERM EXAMINATION
MACROECONOMIC THEORY T. J. KEHOE ECON 85 FALL 7 ANSWERS TO MIDTERM EXAMINATION. (a) Wh an Arrow-Debreu markes sruure fuures markes for goods are open n perod. Consumers rade fuures onras among hemselves.
More informationUniform Topology on Types and Strategic Convergence
Unform Topology on Types and Sraegc Convergence lfredo D Tllo IGIER and IEP Unversà Boccon alfredo.dllo@unboccon. Eduardo Fangold Deparmen of Economcs Yale Unversy eduardo.fangold@yale.edu ugus 7 2007
More informationChapter 6: AC Circuits
Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.
More informationCHAPTER 10: LINEAR DISCRIMINATION
CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g
More informationNew M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study)
Inernaonal Mahemacal Forum, Vol. 8, 3, no., 7 - HIKARI Ld, www.m-hkar.com hp://dx.do.org/.988/mf.3.3488 New M-Esmaor Objecve Funcon n Smulaneous Equaons Model (A Comparave Sudy) Ahmed H. Youssef Professor
More informationJanuary Examinations 2012
Page of 5 EC79 January Examnaons No. of Pages: 5 No. of Quesons: 8 Subjec ECONOMICS (POSTGRADUATE) Tle of Paper EC79 QUANTITATIVE METHODS FOR BUSINESS AND FINANCE Tme Allowed Two Hours ( hours) Insrucons
More informationFoundations of State Estimation Part II
Foundaons of Sae Esmaon Par II Tocs: Hdden Markov Models Parcle Flers Addonal readng: L.R. Rabner, A uoral on hdden Markov models," Proceedngs of he IEEE, vol. 77,. 57-86, 989. Sequenal Mone Carlo Mehods
More informationOn One Analytic Method of. Constructing Program Controls
Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna
More informationLecture 11 SVM cont
Lecure SVM con. 0 008 Wha we have done so far We have esalshed ha we wan o fnd a lnear decson oundary whose margn s he larges We know how o measure he margn of a lnear decson oundary Tha s: he mnmum geomerc
More information8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall
8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model
More informationF-Tests and Analysis of Variance (ANOVA) in the Simple Linear Regression Model. 1. Introduction
ECOOMICS 35* -- OTE 9 ECO 35* -- OTE 9 F-Tess and Analyss of Varance (AOVA n he Smple Lnear Regresson Model Inroducon The smple lnear regresson model s gven by he followng populaon regresson equaon, or
More informationGENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim
Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran
More informationExample: MOSFET Amplifier Distortion
4/25/2011 Example MSFET Amplfer Dsoron 1/9 Example: MSFET Amplfer Dsoron Recall hs crcu from a prevous handou: ( ) = I ( ) D D d 15.0 V RD = 5K v ( ) = V v ( ) D o v( ) - K = 2 0.25 ma/v V = 2.0 V 40V.
More informationLi An-Ping. Beijing , P.R.China
A New Type of Cpher: DICING_csb L An-Png Bejng 100085, P.R.Chna apl0001@sna.com Absrac: In hs paper, we wll propose a new ype of cpher named DICING_csb, whch s derved from our prevous sream cpher DICING.
More informationNormal Random Variable and its discriminant functions
Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The
More informationOnline Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading
Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng
More informationCHAPTER 5: MULTIVARIATE METHODS
CHAPER 5: MULIVARIAE MEHODS Mulvarae Daa 3 Mulple measuremens (sensors) npus/feaures/arbues: -varae N nsances/observaons/eamples Each row s an eample Each column represens a feaure X a b correspons o he
More informationFall 2010 Graduate Course on Dynamic Learning
Fall 200 Graduae Course on Dynamc Learnng Chaper 4: Parcle Flers Sepember 27, 200 Byoung-Tak Zhang School of Compuer Scence and Engneerng & Cognve Scence and Bran Scence Programs Seoul aonal Unversy hp://b.snu.ac.kr/~bzhang/
More informationNational Exams December 2015 NOTES: 04-BS-13, Biology. 3 hours duration
Naonal Exams December 205 04-BS-3 Bology 3 hours duraon NOTES: f doub exss as o he nerpreaon of any queson he canddae s urged o subm wh he answer paper a clear saemen of any assumpons made 2 Ths s a CLOSED
More information, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables
Opmal Conrol Why Use I - verss calcls of varaons, opmal conrol More generaly More convenen wh consrans (e.g., can p consrans on he dervaves More nsghs no problem (a leas more apparen han hrogh calcls of
More informationJ i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.
umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal
More informationRobustness Experiments with Two Variance Components
Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference
More informationII. Light is a Ray (Geometrical Optics)
II Lgh s a Ray (Geomercal Opcs) IIB Reflecon and Refracon Hero s Prncple of Leas Dsance Law of Reflecon Hero of Aleandra, who lved n he 2 nd cenury BC, posulaed he followng prncple: Prncple of Leas Dsance:
More informationA Reinforcement Procedure Leading to Correlated Equilibrium
A Renforcemen Procedure Leadng o Correlaed Equlbrum Sergu Har and Andreu Mas-Colell 2 Cener for Raonaly and Ineracve Decson Theory; Deparmen of Mahemacs; and Deparmen of Economcs, The Hebrew Unversy of
More informationCubic Bezier Homotopy Function for Solving Exponential Equations
Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.
More informationVolatility Interpolation
Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local
More informationDynamic Regressions with Variables Observed at Different Frequencies
Dynamc Regressons wh Varables Observed a Dfferen Frequences Tlak Abeysnghe and Anhony S. Tay Dearmen of Economcs Naonal Unversy of Sngaore Ken Rdge Crescen Sngaore 96 January Absrac: We consder he roblem
More informationHEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD
Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,
More informationReactive Methods to Solve the Berth AllocationProblem with Stochastic Arrival and Handling Times
Reacve Mehods o Solve he Berh AllocaonProblem wh Sochasc Arrval and Handlng Tmes Nsh Umang* Mchel Berlare* * TRANSP-OR, Ecole Polyechnque Fédérale de Lausanne Frs Workshop on Large Scale Opmzaon November
More informationThe Finite Element Method for the Analysis of Non-Linear and Dynamic Systems
Swss Federal Insue of Page 1 The Fne Elemen Mehod for he Analyss of Non-Lnear and Dynamc Sysems Prof. Dr. Mchael Havbro Faber Dr. Nebojsa Mojslovc Swss Federal Insue of ETH Zurch, Swzerland Mehod of Fne
More informationLaser Interferometer Space Antenna (LISA)
aser nerferomeer Sace Anenna SA Tme-elay nerferomery wh Movng Sacecraf Arrays Massmo Tno Je Proulson aboraory, Calforna nsue of Technology GSFC JP 8 h GWAW, ec 7-0, 00, Mlwaukee, Wsconsn WM Folkner e al,
More informationグラフィカルモデルによる推論 確率伝搬法 (2) Kenji Fukumizu The Institute of Statistical Mathematics 計算推論科学概論 II (2010 年度, 後期 )
グラフィカルモデルによる推論 確率伝搬法 Kenj Fukuzu he Insue of Sascal Maheacs 計算推論科学概論 II 年度 後期 Inference on Hdden Markov Model Inference on Hdden Markov Model Revew: HMM odel : hdden sae fne Inference Coue... for any Naïve
More informationTjalling C. Koopmans Research Institute
Tallng C Koopmans Research Insue Tallng C Koopmans Research Insue Urech School of Economcs Urech Unversy Janskerkhof 3 BL Urech The Neherlands elephone +3 30 3 9800 fax +3 30 3 7373 webse wwwkoopmansnsueuunl
More informationChapters 2 Kinematics. Position, Distance, Displacement
Chapers Knemacs Poson, Dsance, Dsplacemen Mechancs: Knemacs and Dynamcs. Knemacs deals wh moon, bu s no concerned wh he cause o moon. Dynamcs deals wh he relaonshp beween orce and moon. The word dsplacemen
More information2. SPATIALLY LAGGED DEPENDENT VARIABLES
2. SPATIALLY LAGGED DEPENDENT VARIABLES In hs chaper, we descrbe a sascal model ha ncorporaes spaal dependence explcly by addng a spaally lagged dependen varable y on he rgh-hand sde of he regresson equaon.
More informationJohn Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany
Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy
More informationNATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours
NATONAL UNVERSTY OF SNGAPORE PC5 ADVANCED STATSTCAL MECHANCS (Semeser : AY 1-13) Tme Allowed: Hours NSTRUCTONS TO CANDDATES 1. Ths examnaon paper conans 5 quesons and comprses 4 prned pages.. Answer all
More informationBundling with Customer Self-Selection: A Simple Approach to Bundling Low Marginal Cost Goods On-Line Appendix
Bundlng wh Cusomer Self-Selecon: A Smple Approach o Bundlng Low Margnal Cos Goods On-Lne Appendx Lorn M. H Unversy of Pennsylvana, Wharon School 57 Jon M. Hunsman Hall Phladelpha, PA 94 lh@wharon.upenn.edu
More informationA Cell Decomposition Approach to Online Evasive Path Planning and the Video Game Ms. Pac-Man
Cell Decomoson roach o Onlne Evasve Pah Plannng and he Vdeo ame Ms. Pac-Man reg Foderaro Vram Raju Slva Ferrar Laboraory for Inellgen Sysems and Conrols LISC Dearmen of Mechancal Engneerng and Maerals
More informationLecture 2 M/G/1 queues. M/G/1-queue
Lecure M/G/ queues M/G/-queue Posson arrval process Arbrary servce me dsrbuon Sngle server To deermne he sae of he sysem a me, we mus now The number of cusomers n he sysems N() Tme ha he cusomer currenly
More informationII The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions
II The Z Trnsfor Tocs o e covered. Inroducon. The Z rnsfor 3. Z rnsfors of eleenry funcons 4. Proeres nd Theory of rnsfor 5. The nverse rnsfor 6. Z rnsfor for solvng dfference equons II. Inroducon The
More informationTesting a new idea to solve the P = NP problem with mathematical induction
Tesng a new dea o solve he P = NP problem wh mahemacal nducon Bacground P and NP are wo classes (ses) of languages n Compuer Scence An open problem s wheher P = NP Ths paper ess a new dea o compare he
More informationLOCATION CHOICE OF FIRMS UNDER STACKELBERG INFORMATION ASYMMETRY. Serhij Melnikov 1,2
TRANPORT & OGITI: he Inernaonal Journal Arcle hsory: Receved 8 March 8 Acceed Arl 8 Avalable onlne 5 Arl 8 IN 46-6 Arcle caon nfo: Melnov,., ocaon choce of frms under acelberg nformaon asymmery. Transor
More informationAppendix H: Rarefaction and extrapolation of Hill numbers for incidence data
Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs
More informationTHE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS
THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he
More informationChapter 7: Solving Trig Equations
Haberman MTH Secion I: The Trigonomeric Funcions Chaper 7: Solving Trig Equaions Le s sar by solving a couple of equaions ha involve he sine funcion EXAMPLE a: Solve he equaion sin( ) The inverse funcions
More information