IEICE TRANS.??, VOL.Exx??, NO.xx XXXX 200x 1
|
|
- Rosalyn Green
- 6 years ago
- Views:
Transcription
1 IEICE TRANS.??, VOL.Exx??, NO.xx XXXX 00x PAPER Specal Secto o Iformato Theory ad Its Applcatos A Fudametal Iequalty for Lower-boudg the Error Probablty for Classcal ad Quatum ultple Access Chaels ad Its Applcatos Takuya KUBO, Nomember ad Hrosh NAGAOKA, ember arxv: v [cs.it 4 ar 05 SUARY I the study of the capacty problem for multple access chaels (ACs), a lower boud o the error probablty obtaed by Ha plays a crucal role the coverse parts of several kds of chael codg theorems the formato-spectrum framework. Recetly, Yag ad Oohama showed a tghter boud tha the Ha boud by meas of Polyasky s coverse. I ths paper, we gve a ew boud whch geeralzes ad stregthes the Yag-Oohama boud, ad demostrate that the boud plays a fudametal role dervg extesos of several kow bouds. I partcular, the Yag-Oohama boud s geeralzed to two dfferet drectos;.e, to geeral put dstrbutos ad to geeral ecoders. I addto we exted these bouds to the quatum ACs ad apply them to the coverse problems for several formato-spectrum settgs. key words: quatum chael, multple access chael, error probablty, formato-spectrum. Itroducto The capacty problem for multple access chaels(acs) has bee a mportat topc sce Shao [9 studed t. Ths problem s studed for several kds of settgs. For stace, the classcal case, Ahlswede [ foud the sgleletterzed capacty rego for statoary ad memoryless chaels, Ha [[3 foud the capacty rego for the geeral chaels by meas of formato spectrum method, ad Wter [ foud that for statoary ad memoryless chaels the quatum case. However, there rema some fudametal problems to be solved, cludg the expoetal covergece of the error probablty the strogcoverse rego for statoary memoryless chaels ad the geeral formato-spectrum formula for the capacty rego the quatum case. So we stll eed to look for good lower bouds o the error probablty. I ths paper, we dscuss lower bouds o the error probablty for the followg three settgs, whch are smlar but slghtly dfferet from each other. Settg LetX,X adybe arbtrary dscrete sets o whch a put dstrbuto p(x, x ) ad a chael W(y x, x ) are gve. For a reversed chaelg(x, x y), whch meas the probablty of decodg (or estmatg) the put (x, x ) from the observed outputy, the error probablty s defed by auscrpt receved February 3, 05. auscrpt revsed February 3, 05. The authors are wth the Graduate School of Iformato Systems, The Uversty of Electro-Commucatos. DOI: 0.587/tras.E0.??. Pe(g) := x,x,y p(x, x )W(y x, x )g(x, x y). () Settg LetX,X adybe arbtrary dscrete sets o whch a chael W(y x, x ) s gve. Gve a par of message sets ad wth = ad = together wth ecoders f (x m ) ad f (x m ), whch meas the probabltes of ecodg the message m ad m to the puts x ad x respectvely, we defe the error probablty for a arbtrary decoder g(m, m y) by Pe(g) := m,m f (x m ) f (x m )W(y x, x )g(m, m y). () x,x,y Settg 3 LetX,X ady be arbtrary dscrete set s o whch a chael W(y x, x ) s gve. Gve a par of codebooks C X adc X wth C = ad C =, we defe the error probablty for a arbtrary decoder g(m, m y) by Pe(g) := x C,x C,y W(y x, x )g(x, x y). (3) Note that Settg 3 ca be regarded as specal cases of both Settg ad Settg. That s, Settg 3 s obtaed by restrctg p(x, x ) to the product of the uform dstrbutos o the codebooks Settg, ad s obtaed by restrctg ecoders f, f to be determstc ad jectve Settg. I the study of the capacty problem, Settg 3 have bee maly dealt wth so far, as metoed below for [[3 ad [, whle Poor ad Verdú [8 dscussed a lower boud of the error probablty Settg ad Polyasky [7 used Settg hs meta-coverse argumet. I Settg 3, Ha [[3 showed the followg lower boud, whch s kow as the Ha boud. For a arbtrary postve umber γ, t holds that Pe(g) Pr{(X, X, Y) L L L 3 } 3γ, (4) Copyrght c 00x The Isttute of Electrocs, Iformato ad Commucato Egeers
2 IEICE TRANS.??, VOL.Exx??, NO.xx XXXX 00x Pr deotes the probablty defed by the jot dstrbuto p(x, x,y)= p u, (x )p u, (x )W(y x, x ) for the uform dstrbutos p u, ad p u, o the codebooks, ad L :={(x, x,y) W(y x, x )γ p(y x )}, (5) L :={(x, x,y) W(y x, x )γ p(y x )}, (6) L 3 :={(x, x,y) W(y x, x )γ 3 p(y)}, (7) 3 :=. (8) Ths boud s a AC exteso of the Verdú-Ha boud [0 ad plays a crucal role the coverse parts of several codg theorems for geeral AC chaels. Recetly Yag ad Oohama [ showed a tghter boud as follows. For a arbtrary codtoal dstrbuto q(y x, x ), a arbtrary dstrbutoπo{,, 3}, ad a arbtrary postve umberγ, t holds that Pe(g) Pr{(X, X, Y) L} γ 3 = π, (9) L :={(x, x,y) W(y x, x )γ q(y x, x )}, (0) q(y x, x )=π q(y x )+π q(y x )+π 3 q(y), () ad q(y x ), q(y x ) ad q(y) are the codtoal ad margal dstrbutos defed from the jot dstrbuto q(x, x,y)= p u, (x )p u, (x )q(y x, x ). () If we setπ = j j, γ = γ j j ad q(y x, x ) = p(y x, x ), we ca rewrte (9) ad (0) as follows. Pe(g) Pr{(X, X, Y) L} 3γ, (3) L={(x, x,y) W(y x, x )γ( p(y x )+ p(y x )+ 3 p(y))}. (4) Sce L L L 3 L, (9) s tghter tha (4). I what follows, we frst show a exteso of the Yag- Oohama boud (9) as Theorem secto, the Yag-Oohama boud s exteded from Settg 3 to Settg ad, addto, s slghtly stregtheed as s see subsecto 3.. We also see subsecto 3. that the theorem yelds a AC verso of the Poor-Verdú boud. I secto 4, we use Theorem aga to obta a exteso of the Yag- Oohama boud to Settg. I secto 5, we show that these results are aturally exteded to the quatum case. Lastly secto 6, we apply them to obta some asymptotc results whch correspod to the coverse parts of the geeral capacty theorems obtaed by Ha the classcal case. Cocludg remarks are gve secto 7.. A fudametal equalty o the error probablty for the classcal ACs The followg equalty plays a fudametal role ths paper. Theorem. I Settg gve secto, for a arbtrary decoderg, arbtraryα,α,α 3 0, a arbtrary probablty dstrbuto q(y) o Y, ad arbtrary oegatve-valued fuctos q (x,y), q (x,y) satsfyg that q(y) q (x,y) ad q(y) q (x,y) ( x, x,y), we have Pe(g) α [p(x, x.y) q α (x, x,y) +, x,x,y (5) q α (x, x,y)=α q (x,y)+α q (x,y)+α 3 q(y), (6) [t + = max{0, t}. (t R) (7) Proof. As the proof of Neyma-Pearso s Lemma, t follows from 0g(x, x y) that [(p(x, x.y) q α (x, x,y) + x,x,y {(p(x, x.y) q α (x, x,y)}g(x, x y) x,x,y Pe(g) α x,x,y q(y)g(x, x y) = Pe(g) α, (8) the secod equalty follows from q(y) q (x,y) ad q(y) q (x,y). 3. Corollares of Theorem Settg 3. A Yag-Oohama-type boud The Yag-Oohama boud s exteded to the geeral put dstrbutos the followg form. Corollary. Pe(g) Pr{p(X, X, Y)q α (X, X, Y)} α (9) Proof. Eq. (9) mmedately follows from (5), sce [p(x, x.y) q α (x, x,y) + x,x,y = (p(x, x.y) q α (x, x,y)) x,x,y x,x,y {p(x, x.y)>q α (x, x,y)} p(x, x.y) {p(x, x.y)>q α (x, x,y)} = Pr{p(X, X, Y)q α (X, X, Y)}, (0) {}s the dcator fucto. I Settg 3, the orgal Yag-Oohama boud (3) s obtaed from (9) by settgα =γ π /.
3 KUBO ad NAGAOKA: A FUNDAENTAL INEQUALITY FOR LOWER-BOUNDING THE ERROR PROBABILITY FOR CLASSICAL AND QUANTU ULTIPLE ACCESS CHANNELS AND 3. A Poor-Verdú-type boud Whle Corollary ca be regarded as a AC exteso of the Verdú-Ha boud [0 (or the Hayash-Nagaoka boud [4 the sese that arbtrary output dstrbutos are allowed), the followg boud correspods to the Poor-Verdú boud [8. Corollary. Pe(g) ( ) α Pr{p(X, X, Y) p α (X, X, Y)}, () p α (x, x,y)=α p(x,y)+α p(x,y)+α 3 p(y), () ad p(x,y), p(x,y) ad p(y) are margal dstrbutos defed from the jot dstrbuto p(x, x,y). Proof. If q = p, the rght had sde of (5) s rewrtte as (p(x, x.y) p α (x, x,y)) x,x,y ( α ) {p(x, x.y)> p α (x, x,y)} p(x, x.y) x,x,y {p(x, x.y)> p α (x, x,y)}, (3) the equalty follows from p(x, x,y) p(x, x,y) p(x,y), ad p(x, x,y) p(x,y). 4. Corollares of Theorem Settg p(y), A exteso of the Yag-Oohama boud to Settg, geeral stochastc ecoders are allowed, s also derved from Theorem as follows. Corollary 3. I Settg, for a arbtrary decoderg, arbtraryγ,γ,γ 3 0, a arbtrary dstrbuto q oy, ad arbtrary codtoal dstrbutos q (y x ), q (y x ) satsfyg that q(y) q (m,y) := x f (x m )q (y x ), (4) q(y) q (m,y) := x f (x m )q (y x ), (5) we have Pe(g) x,x,y γ ( m, m,y) p (x )p (x ) [ W(y x, x ) q γ (y x, x ) +, (6) q γ (y x, x )=γ q(y x )+γ q(y x )+γ 3 q(y), (7) p (x )= f (x m ), (8) m p (x )= f (x m ), (9) m 3 =. (30) Proof. Let a chael V from toybe defed by V(y m, m )= f (x m ) f (x m )W(y x, x ). (3) x,x The, replacgx,x ad W wth,, ad V Theorem ad lettg the put dstrbuto be uform o, we have Pe(g) m,m,y [ γ V(y m, m ) q γ (m, m,y), (3) + q γ (m, m,y)= γ q (m,y)+ γ q (m,y)+ γ 3 3 q(y). From the covexty of t [t +, we have Pe(g) m,m,x,x,y γ f (x m ) f (x m ) (33) [W(y x, x ) q γ (y x, x ) +. (34) Ths equalty mmedately derves the followg boud, whch s the drect exteso of the Yag-Oohama boud to Settg. Corollary 4. I Settg, for a arbtrary decoder g, a arbtrary dstrbutoπo{,, 3}, a arbtrary umberγ 0, ad a arbtrary chael q(y x, x ), we have π Pe(g) Pr{W(Y X, X )γ q(y X, X )} γ, (35) the radom varables X, X, ad Y are defed by the jot dstrbuto p(x, x,y)= f (x m ) f (x m )W(y x, x ), m,m (36) ad q s defed by ().
4 4 IEICE TRANS.??, VOL.Exx??, NO.xx XXXX 00x 5. Lower bouds o the error probablty for the quatum ACs I ths secto we exted the argumets of prevous sectos to classcal-quatum ACs. I the sgle access case, Hayash ad Nagaoka [4 exteded the Verdú-Ha boud to the quatum case, ad the preset authors [5, [6 exteded the Poor-Verdú boud. Applyg a smlar argumet to the oes developed there, we exted Theorem as preseted Theorem, from whch the correspodg results to Corollares -3 mmedately follow. We beg wth rewrtg Settg ad Settg to the quatum stuato. Settg 3 s omtted sce t s cluded Settg ad Settg. Settg Q LetX,X be arbtrary dscrete sets o whch a put dstrbuto p(x, x ) s gve. LetH be a arbtrary Hlbert space ads(h) be the set of desty operators oh ad W : X X S(H) be a classcal-quatum chael (a quatum chael, for short). Whe a POV (Postve Operator-Valued easure) Y={Y x,x }, whch satsfes that x,x Y x,x = I ad Y x,x 0 ( x, x ), represets a decodg (or estmatg) process, the error probablty s defed by Pe(Y) := p(x, x )Tr[W x,x Y x,x. (37) x,x Settg Q LetX,X be arbtrary dscrete sets,h be a arbtrary Hlbert space ad a quatum chael W :X X S(H) s gve. As Settg, gve a par of message sets ad wth = ad = together wth ecoders f (x m ) ad f (x m ), whch meas the probabltes of ecodg the message m ad m to the puts x ad x respectvely, we defe the error probablty for a arbtrary POV Y whose dexes are by Pe(Y) := m,m f (x m ) f (x m )Tr[W x,x Y m,m. (38) x,x Theorem s exteded as follows. Theorem. I Settg Q, for a arbtrary POV Y, arbtraryα,α,α 3 0, a arbtrary desty operatorσoh, ad arbtrary postve semdefte operatorsσ x,σ x satsfyg thatσ σ x adσ σ x ( x, x ), we have Pe(Y) α Tr[(p(x, x )W x,x σ α,x,x ) +, x,x (39) σ α,x,x =α σ x +α σ x +α 3 σ, (40) A + := A{A 0}. (4) Here ad the sequel, we use the otato{a B}={B A} to mea a projector oh whch s defed as follows. Whe A - B s spectrum-decomposed as A B= λ E, (4) {AB} := E. (43) :λ 0 Proof. As the classcal case, t follows from 0Y x,x I that Tr[(p(x, x )W x,x σ α,x,x ) + x,x Tr[(p(x, x )W x,x σ α,x,x )Y x,x x,x Pe(Y) α Tr[σY x,x x,x = Pe(Y) α, (44) the secod equalty follows fromσ σ x adσ σ x. Obvously, as Theorem derves Corollares ad, Theorem derves the followg corollares. Corollary 5. Pe(Y) p(x, x )Tr[W x,x {p(x, x )W x,x σ α,x,x } x,x α (45) Corollary 6. Pe(Y) ( α ) p(x, x )Tr[W x,x {p(x, x )W x,x W α,x,x }, x,x (46) W α,x,x =α W p,x +α W x,p+α 3 W p, (47) W p := p(x, x )W x,x, (48) x,x W p,x := p(x, x )W x,x, (49) x W x,p := p(x, x )W x,x. (50) x Corollary 5 s a AC exteso of the Hayash- Nagaoka boud, ad Corollary 6 s a quatum AC exteso of the Poor-Verdú boud.
5 KUBO ad NAGAOKA: A FUNDAENTAL INEQUALITY FOR LOWER-BOUNDING THE ERROR PROBABILITY FOR CLASSICAL AND QUANTU ULTIPLE ACCESS CHANNELS AND Corollary 3 s also exteded to the followg, whch ca be proved almost parallel wth the classcal oe, otg that the covexty of t [t + should be replaced wth the covexty of A Tr[A +. Corollary 7. I Settg Q, for a arbtrary POV Y, arbtraryγ,γ,γ 3 0, a arbtrary desty operatorσ oh, ad arbtrary desty operatorsσ x,σ x satsfyg that σ σ m := f (x m )σ x, x (5) σ σ m := f (x m )σ x, x (5) we have Pe(Y) γ ( m, m ) p (x )p (x )Tr[(W x,x σ γ,x,x ) +, (53) x,x σ γ,x,x =γ σ x +γ σ x +γ 3 σ, (54) p (x )= f (x m ), (55) m p (x )= f (x m ). (56) m 6. Applcatos of Theorem to the quatum formato spectrum settg,m() I ths secto, we show applcatos of Theorem to the quatum AC codg problems; the coverse parts of the ε-capacty rego problem ad the strog coverse rego problem, whch Ha [ [3 showed the classcal case. Let us troduce the settg of the quatum AC codg problem. Let X = {X () } = ad X = {X () } = be sequeces of dscrete sets, adh = {H () } = be a sequece of Hlbert spaces, for whch a sequece of quatum ACs W ={ :X () X () S(H () )} = s gve. Suppose that, for each, a par of ecoders ad a decoder are gve terms of codtoal probablty dstrbutos f ( m() ), f ( m() ) ad a POV Y() ={Y () } respectvely, {,..., () }, m() {,..., () }. The error probablty s the defed as follows: Pe () (Y () )=,m() () () Tr[ f ( m() ) f ( m() ) Y (),m(). (57) Here, we call a trple of ecoders ad decoder ( f (), f (), Y() ) whose error probablty equalsε a (, (), (),ε )-code. Now, we troduce the ε-capacty rego C(ε W). Defto. The ε-capacty rego C(ε W) s defed as C(ε W) :={(R, R ) {(, (), (),ε )-code} = s.t. lm supε ε, lm f lm f log () R, log () R }. (58) We also troduce C ( W) whch represets the complemet of the strog coverse rego. Defto. C ( W) :={(R, R ) {(, (), (),ε )-code} = s.t. lm f ε <, lm f lm f log () R, log () R }. (59) Next, we troduce the followg quattes. Defto 3. K(R, R p, p, σ) := lm sup,x() (x() )p() (x() )Tr[W() { e R σ () + e R σ () + e (R +R ) σ () }, (60) K (R, R p, p, σ) := lm f {,x() e R σ () (x() )p() (x() )Tr[W() + e R σ () + e (R +R ) σ () }, (6) p ={ } = ad p ={ } = are sequeces of probablty dstrbutos o X ad X, ad σ s a sequece of a trple of desty operators(σ (),σ (),σ () ) whch satsfes that σ () = ad σ () = for each. (x() (x() )σ() )σ() Wth these otatos, we have Theorem 3. (6) (63)
6 6 C(ε W) Cl({(R, R ) K(R, R p, p, σ)ε}), p, p σ Cl( ) deotes the closure operato. (64) Proof. If (R, R ) C(ε W), the from the dfto of C(ε W) there exsts a sequece of (, (), (),ε )-codes satsfyg that () e (R γ), (65) () e (R γ), (66) for a arbtrary postve umber γ ad all suffcetly large, ad lm supε ε. (67) Usg these codes, settg the sequeces of the put dstrbutos as (x() )= f () () (x() m() ), (68) (x() )= () f () (x() m() ). (69) Now, from Corollary 7, for arbtrary σ satsfyg (6) ad (63), we have ε 3e γ (x() )p() (x() ) [( Tr ( e γ () σ() + () σ() )) + () () σ(). + (70) From (65), (66) ad from the fact that for arbtrary Hermta operators A, B, Tr[A + Tr[B + f A B, we have ε 3e γ [( (x() )p() (x() )Tr A () B () [( (x() )p() (x() )Tr [ (x() )p() (x() )Tr = e (R γ) σ () = e (R γ) σ () ) A () + ) B () + { > B () (7) + e (R γ) σ () + e (R +R 3γ) σ (), (7) + e (R γ) σ () + e (R +R 4γ) σ (). (73) }, Therefore, t follows that [ ε (x() )p() (x() )Tr Hece, from (67) ad (74) we have IEICE TRANS.??, VOL.Exx??, NO.xx XXXX 00x { } B () 3e γ. (74) K(R γ, R γ p, p, σ)lm supε ε. (75) Sceγs arbtrary, (75) mples that (R, R ) Cl({(R, R ) K(R, R p, p, σ)ε}). (76) We also have Theorem 4. C ( W) Cl({(R, R ) K (R, R p, p, σ)<}). p, p σ (77) Proof. Let ( ) c deote the complemet adr( p, p, σ) := Cl({(R, R ) K (R, R p, p, σ) < }). Suppose that for arbtrary p, p, there exsts σ satsfyg that (R, R ) R( p, p, σ) c, whch meas that (R, R ) belogs to the rght had sde of (77). The (R γ, R γ) s also R( p, p, σ) c for suffcetly small postve umberγ sce R( p, p, σ) c s ope. Ths mples that K (R γ, R γ p, p, σ)=. (78) O the other had, for (, (), (),ε )-codes whch satsfes (65) ad (66) forγwhch s used (78) ad for all suffcetly large, the sequeces of the put dstrbutos whch are set as (68) ad (69) are clearly depedet. Furthermore, for such codes we have (74). Hece, from (74) ad (78) we have lm ε =. (79) Ths meas that (R γ, R γ) C ( W) c. For arbtrary postve umbers S < S ad S < S, f (S, S ) C ( W) c, the clearly (S, S ) C ( W) c from the defto of C ( W). Therefore, (R, R ) C ( W) c. The rest of ths secto s devoted to show how our results lead to the coverse parts of classcal capacty theorems obtaed by Ha [ [3. Frst, let W p = {( p(),,p() :=,p() :=,p(), )} be defed as (x() )p() (x() (x() )W )W(), (80), (8)
7 KUBO ad NAGAOKA: A FUNDAENTAL INEQUALITY FOR LOWER-BOUNDING THE ERROR PROBABILITY FOR CLASSICAL AND QUANTU ULTIPLE ACCESS CHANNELS AND := (x() )W. (8) The from Theorems 3 ad 4 we have C(ε W) Cl({(R, R ) K(R, R p, p, W p )ε}) (83) p, p ad C ( W) Cl({(R, R ) K (R, R p, p, W p )<}). (84) p, p I the classcal case, recallg that the Yag-Oohama boud mples the Ha boud, we ca easly show that ad K(R, R p, p, W p ) J(R, R X, X ) K (R, R p, p, W p ) J (R, R X, X ), J ad J are dfed [ [3. Therefore we have {(R, R ) Kε} {(R, R ) Jε}, (85) {(R, R ) K < } {(R, R ) J < }. (86) Note that Ha also proved ther drect parts [ [3, whch establsh capacty formulas: C(ε W)= Cl({(R, R ) Jε}), (87) p, p C ( W)= Cl({(R, R ) J < }), (88) p, p although (88) s ot explctly preseted [ [3. As a cosequece, we have Cl({(R, R ) Kε})= Cl({(R, R ) Jε}) p, p p, p (89) Cl({(R, R ) K < })= Cl({(R, R ) J < }) p, p p, p (90) geeral quatum ACs as applcatos of the fudametal equalty. It however remas to obta a good upper boud o the error probablty order to determe these regos. Refereces [ Ahlswede., R., ult-way commucato chaels., d It. Symp. If. Theory, pp Hugara Academy of Sceces, Budapest, 97. [ Ha, T. S., A formato-spectrum approach to capacty theorems for the geeral multple-access chael, IEEE Tras. Iform. Theory, vol. 44, o. 7, pp , 998. [3 Ha, T. S., Iformato-Spectrum ethods Iformato Theory, Sprger, 003. [4 Hayash,. ad Nagaoka, H., Geeral formulas for capacty of classcal-quatum chaels, IEEE Tras. Iform. Theory, vol. 49, o. 7, pp , 003. [5 Kubo, T. ad Nagaoka, H., Lower bouds of the error probablty estmatg classcal ad quatum states, QIT5 ( Japaese), Osaka, Japa, November 0. [6 Kubo, T. ad Nagaoka, H., Lower Bouds o the Error Probablty Classcal ad Quatum State Dscrmato, ISITA0, Hoolulu, USA, October 0. [7 Polyasky, Y., Chael codg: o-asymptotc fudametal lmts, Ph. D. thess, Departmet of Electrcal Egeerg, Prceto Uversty, 00. [8 Poor, H. V. ad Verdú, S., A lower boud o the probablty of error multhypothess testg, IEEE Tras. Iform. Theory, vol. 4, o. 6, pp , 995. [9 Shao, C. E., Two-way commucato chaels, 4th Berkeley Symp. ath. Stat. Prob., vol., pp Uversty of Calfora Press, Berkeley, CA, 96. [0 Verdú, S. ad Ha, T. S., A Geeral formula for chael capacty, IEEE Tras. Iform. Theory, vol. 40, o. 4, pp , 994. [ Wter, A., The Capacty of the Quatum ultple-access Chael, IEEE Tras. Iform. Theory, vol. 47, o. 7, pp , 00. [ Yag, H. ad Oohama, Y., Fte Blocklegth Aalyss for ultple Access Chaels ad Composte Hypothess Testg, SITA0 ( Japaese), Ota, Japa, December 0. the classcal case. I the quatum case, o the other had, sce we have ot prove ther drect parts, t s ot clear whether Theorem 3 ad 4 are tght. 7. Cocludg Remarks We have dscussed lower bouds o the error probablty for ACs several settgs. We have obtaed a fudametal equalty the classcal case (Theorem ) ad the quatum case (Theorem ). Usg the equalty the Yag-Oohama boud has bee geeralzed ad stregtheed several drectos ad exteded to the quatum case. We have also show coverse results o theε-capacty rego problem ad the strog coverse rego problem for
Complete Convergence and Some Maximal Inequalities for Weighted Sums of Random Variables
Joural of Sceces, Islamc Republc of Ira 8(4): -6 (007) Uversty of Tehra, ISSN 06-04 http://sceces.ut.ac.r Complete Covergece ad Some Maxmal Iequaltes for Weghted Sums of Radom Varables M. Am,,* H.R. Nl
More informationChapter 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 informationA tighter lower bound on the circuit size of the hardest Boolean functions
Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the
More informationResearch Article A New Iterative Method for Common Fixed Points of a Finite Family of Nonexpansive Mappings
Hdaw Publshg Corporato Iteratoal Joural of Mathematcs ad Mathematcal Sceces Volume 009, Artcle ID 391839, 9 pages do:10.1155/009/391839 Research Artcle A New Iteratve Method for Commo Fxed Pots of a Fte
More informationρ < 1 be five real numbers. The
Lecture o BST 63: Statstcal Theory I Ku Zhag, /0/006 Revew for the prevous lecture Deftos: covarace, correlato Examples: How to calculate covarace ad correlato Theorems: propertes of correlato ad covarace
More informationChapter 4 Multiple Random Variables
Revew for the prevous lecture: Theorems ad Examples: How to obta the pmf (pdf) of U = g (, Y) ad V = g (, Y) Chapter 4 Multple Radom Varables Chapter 44 Herarchcal Models ad Mxture Dstrbutos Examples:
More informationLecture 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 informationComplete Convergence for Weighted Sums of Arrays of Rowwise Asymptotically Almost Negative Associated Random Variables
A^VÇÚO 1 32 ò 1 5 Ï 2016 c 10 Chese Joural of Appled Probablty ad Statstcs Oct., 2016, Vol. 32, No. 5, pp. 489-498 do: 10.3969/j.ss.1001-4268.2016.05.005 Complete Covergece for Weghted Sums of Arrays of
More informationOn generalized fuzzy mean code word lengths. Department of Mathematics, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
merca Joural of ppled Mathematcs 04; (4): 7-34 Publshed ole ugust 30, 04 (http://www.scecepublshggroup.com//aam) do: 0.648/.aam.04004.3 ISSN: 330-0043 (Prt); ISSN: 330-006X (Ole) O geeralzed fuzzy mea
More informationFunctions 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 informationPart 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))
art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the
More informationStrong Convergence of Weighted Averaged Approximants of Asymptotically Nonexpansive Mappings in Banach Spaces without Uniform Convexity
BULLETIN of the MALAYSIAN MATHEMATICAL SCIENCES SOCIETY Bull. Malays. Math. Sc. Soc. () 7 (004), 5 35 Strog Covergece of Weghted Averaged Appromats of Asymptotcally Noepasve Mappgs Baach Spaces wthout
More informationON THE LOGARITHMIC INTEGRAL
Hacettepe Joural of Mathematcs ad Statstcs Volume 39(3) (21), 393 41 ON THE LOGARITHMIC INTEGRAL Bra Fsher ad Bljaa Jolevska-Tueska Receved 29:9 :29 : Accepted 2 :3 :21 Abstract The logarthmc tegral l(x)
More informationECE 729 Introduction to Channel Coding
chaelcodg.tex May 4, 2006 ECE 729 Itroducto to Chael Codg Cotets Fudametal Cocepts ad Techques. Chaels.....................2 Ecoders.....................2. Code Rates............... 2.3 Decoders....................
More information3. Basic Concepts: Consequences and Properties
: 3. Basc Cocepts: Cosequeces ad Propertes Markku Jutt Overvew More advaced cosequeces ad propertes of the basc cocepts troduced the prevous lecture are derved. Source The materal s maly based o Sectos.6.8
More informationA Remark on the Uniform Convergence of Some Sequences of Functions
Advaces Pure Mathematcs 05 5 57-533 Publshed Ole July 05 ScRes. http://www.scrp.org/joural/apm http://dx.do.org/0.436/apm.05.59048 A Remark o the Uform Covergece of Some Sequeces of Fuctos Guy Degla Isttut
More informationX X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then
Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers
More informationX 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 informationEntropies & Information Theory
Etropes & Iformato Theory LECTURE II Nlajaa Datta Uversty of Cambrdge,U.K. See lecture otes o: http://www.q.damtp.cam.ac.uk/ode/223 quatum system States (of a physcal system): Hlbert space (fte-dmesoal)
More informationAssignment 5/MATH 247/Winter Due: Friday, February 19 in class (!) (answers will be posted right after class)
Assgmet 5/MATH 7/Wter 00 Due: Frday, February 9 class (!) (aswers wll be posted rght after class) As usual, there are peces of text, before the questos [], [], themselves. Recall: For the quadratc form
More informationD. VQ WITH 1ST-ORDER LOSSLESS CODING
VARIABLE-RATE VQ (AKA VQ WITH ENTROPY CODING) Varable-Rate VQ = Quatzato + Lossless Varable-Legth Bary Codg A rage of optos -- from smple to complex A. Uform scalar quatzato wth varable-legth codg, oe
More informationA New Measure of Probabilistic Entropy. and its Properties
Appled Mathematcal Sceces, Vol. 4, 200, o. 28, 387-394 A New Measure of Probablstc Etropy ad ts Propertes Rajeesh Kumar Departmet of Mathematcs Kurukshetra Uversty Kurukshetra, Ida rajeesh_kuk@redffmal.com
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationBounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy
Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled
More informationResearch Article Some Strong Limit Theorems for Weighted Product Sums of ρ-mixing Sequences of Random Variables
Hdaw Publshg Corporato Joural of Iequaltes ad Applcatos Volume 2009, Artcle ID 174768, 10 pages do:10.1155/2009/174768 Research Artcle Some Strog Lmt Theorems for Weghted Product Sums of ρ-mxg Sequeces
More informationExtreme Value Theory: An Introduction
(correcto d Extreme Value Theory: A Itroducto by Laures de Haa ad Aa Ferrera Wth ths webpage the authors ted to form the readers of errors or mstakes foud the book after publcato. We also gve extesos for
More informationPROJECTION PROBLEM FOR REGULAR POLYGONS
Joural of Mathematcal Sceces: Advaces ad Applcatos Volume, Number, 008, Pages 95-50 PROJECTION PROBLEM FOR REGULAR POLYGONS College of Scece Bejg Forestry Uversty Bejg 0008 P. R. Cha e-mal: sl@bjfu.edu.c
More informationD KL (P Q) := p i ln p i q i
Cheroff-Bouds 1 The Geeral Boud Let P 1,, m ) ad Q q 1,, q m ) be two dstrbutos o m elemets, e,, q 0, for 1,, m, ad m 1 m 1 q 1 The Kullback-Lebler dvergece or relatve etroy of P ad Q s defed as m D KL
More informationResearch Article Multidimensional Hilbert-Type Inequalities with a Homogeneous Kernel
Hdaw Publshg Corporato Joural of Iequaltes ad Applcatos Volume 29, Artcle ID 3958, 2 pages do:.55/29/3958 Research Artcle Multdmesoal Hlbert-Type Iequaltes wth a Homogeeous Kerel Predrag Vuovć Faculty
More informationLecture 02: Bounding tail distributions of a random variable
CSCI-B609: A Theorst s Toolkt, Fall 206 Aug 25 Lecture 02: Boudg tal dstrbutos of a radom varable Lecturer: Yua Zhou Scrbe: Yua Xe & Yua Zhou Let us cosder the ubased co flps aga. I.e. let the outcome
More information{ }{ ( )} (, ) = ( ) ( ) ( ) 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 informationCHAPTER 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 information1 Mixed Quantum State. 2 Density Matrix. CS Density Matrices, von Neumann Entropy 3/7/07 Spring 2007 Lecture 13. ψ = α x x. ρ = p i ψ i ψ i.
CS 94- Desty Matrces, vo Neuma Etropy 3/7/07 Sprg 007 Lecture 3 I ths lecture, we wll dscuss the bascs of quatum formato theory I partcular, we wll dscuss mxed quatum states, desty matrces, vo Neuma etropy
More informationBayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information
Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst
More informationMATH 247/Winter Notes on the adjoint and on normal operators.
MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say
More informationVARIABLE-RATE VQ (AKA VQ WITH ENTROPY CODING)
VARIABLE-RATE VQ (AKA VQ WITH ENTROPY CODING) Varable-Rate VQ = Quatzato + Lossless Varable-Legth Bary Codg A rage of optos -- from smple to complex a. Uform scalar quatzato wth varable-legth codg, oe
More informationChain Rules for Entropy
Cha Rules for Etroy The etroy of a collecto of radom varables s the sum of codtoal etroes. Theorem: Let be radom varables havg the mass robablty x x.x. The...... The roof s obtaed by reeatg the alcato
More informationPoint Estimation: definition of estimators
Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.
More informationIntroduction 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 informationNon-uniform Turán-type problems
Joural of Combatoral Theory, Seres A 111 2005 106 110 wwwelsevercomlocatecta No-uform Turá-type problems DhruvMubay 1, Y Zhao 2 Departmet of Mathematcs, Statstcs, ad Computer Scece, Uversty of Illos at
More informationLebesgue Measure of Generalized Cantor Set
Aals of Pure ad Appled Mathematcs Vol., No.,, -8 ISSN: -8X P), -888ole) Publshed o 8 May www.researchmathsc.org Aals of Lebesgue Measure of Geeralzed ator Set Md. Jahurul Islam ad Md. Shahdul Islam Departmet
More informationExtend the Borel-Cantelli Lemma to Sequences of. Non-Independent Random Variables
ppled Mathematcal Sceces, Vol 4, 00, o 3, 637-64 xted the Borel-Catell Lemma to Sequeces of No-Idepedet Radom Varables olah Der Departmet of Statstc, Scece ad Research Campus zad Uversty of Tehra-Ira der53@gmalcom
More informationCS286.2 Lecture 4: Dinur s Proof of the PCP Theorem
CS86. Lecture 4: Dur s Proof of the PCP Theorem Scrbe: Thom Bohdaowcz Prevously, we have prove a weak verso of the PCP theorem: NP PCP 1,1/ (r = poly, q = O(1)). Wth ths result we have the desred costat
More informationTESTS BASED ON MAXIMUM LIKELIHOOD
ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal
More informationENGI 4421 Joint Probability Distributions Page Joint Probability Distributions [Navidi sections 2.5 and 2.6; Devore sections
ENGI 441 Jot Probablty Dstrbutos Page 7-01 Jot Probablty Dstrbutos [Navd sectos.5 ad.6; Devore sectos 5.1-5.] The jot probablty mass fucto of two dscrete radom quattes, s, P ad p x y x y The margal probablty
More informationLINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX
INTEGERS: ELECTRONIC JOURNAL OF COMBINATORIAL NUMBER THEORY 6 2006, #A12 LINEAR RECURRENT SEQUENCES AND POWERS OF A SQUARE MATRIX Hacèe Belbachr 1 USTHB, Departmet of Mathematcs, POBox 32 El Ala, 16111,
More informationarxiv:math/ v1 [math.gm] 8 Dec 2005
arxv:math/05272v [math.gm] 8 Dec 2005 A GENERALIZATION OF AN INEQUALITY FROM IMO 2005 NIKOLAI NIKOLOV The preset paper was spred by the thrd problem from the IMO 2005. A specal award was gve to Yure Boreko
More informationQ-analogue of a Linear Transformation Preserving Log-concavity
Iteratoal Joural of Algebra, Vol. 1, 2007, o. 2, 87-94 Q-aalogue of a Lear Trasformato Preservg Log-cocavty Daozhog Luo Departmet of Mathematcs, Huaqao Uversty Quazhou, Fua 362021, P. R. Cha ldzblue@163.com
More informationSolving Constrained Flow-Shop Scheduling. Problems with Three Machines
It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632
More informationTHE PROBABILISTIC STABILITY FOR THE GAMMA FUNCTIONAL EQUATION
Joural of Scece ad Arts Year 12, No. 3(2), pp. 297-32, 212 ORIGINAL AER THE ROBABILISTIC STABILITY FOR THE GAMMA FUNCTIONAL EQUATION DOREL MIHET 1, CLAUDIA ZAHARIA 1 Mauscrpt receved: 3.6.212; Accepted
More informationThe Arithmetic-Geometric mean inequality in an external formula. Yuki Seo. October 23, 2012
Sc. Math. Japocae Vol. 00, No. 0 0000, 000 000 1 The Arthmetc-Geometrc mea equalty a exteral formula Yuk Seo October 23, 2012 Abstract. The classcal Jese equalty ad ts reverse are dscussed by meas of terally
More informationSTK4011 and STK9011 Autumn 2016
STK4 ad STK9 Autum 6 Pot estmato Covers (most of the followg materal from chapter 7: Secto 7.: pages 3-3 Secto 7..: pages 3-33 Secto 7..: pages 35-3 Secto 7..3: pages 34-35 Secto 7.3.: pages 33-33 Secto
More informationMarcinkiewicz strong laws for linear statistics of ρ -mixing sequences of random variables
Aas da Academa Braslera de Cêcas 2006 784: 65-62 Aals of the Brazla Academy of Sceces ISSN 000-3765 www.scelo.br/aabc Marckewcz strog laws for lear statstcs of ρ -mxg sequeces of radom varables GUANG-HUI
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationArithmetic Mean and Geometric Mean
Acta Mathematca Ntresa Vol, No, p 43 48 ISSN 453-6083 Arthmetc Mea ad Geometrc Mea Mare Varga a * Peter Mchalča b a Departmet of Mathematcs, Faculty of Natural Sceces, Costate the Phlosopher Uversty Ntra,
More information9 U-STATISTICS. Eh =(m!) 1 Eh(X (1),..., X (m ) ) i.i.d
9 U-STATISTICS Suppose,,..., are P P..d. wth CDF F. Our goal s to estmate the expectato t (P)=Eh(,,..., m ). Note that ths expectato requres more tha oe cotrast to E, E, or Eh( ). Oe example s E or P((,
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Exam: ECON430 Statstcs Date of exam: Frday, December 8, 07 Grades are gve: Jauary 4, 08 Tme for exam: 0900 am 00 oo The problem set covers 5 pages Resources allowed:
More informationLecture 9: Tolerant Testing
Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have
More informationThe 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 informationMultivariate Transformation of Variables and Maximum Likelihood Estimation
Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty
More information18.413: Error Correcting Codes Lab March 2, Lecture 8
18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse
More informationarxiv: v1 [math.st] 24 Oct 2016
arxv:60.07554v [math.st] 24 Oct 206 Some Relatoshps ad Propertes of the Hypergeometrc Dstrbuto Peter H. Pesku, Departmet of Mathematcs ad Statstcs York Uversty, Toroto, Otaro M3J P3, Caada E-mal: pesku@pascal.math.yorku.ca
More informationR t 1. (1 p i ) h(p t 1 ), R t
Multple-Wrte WOM-odes Scott Kayser, Eta Yaaob, Paul H Segel, Alexader Vardy, ad Jac K Wolf Uversty of alfora, Sa Dego La Jolla, A 909 0401, USA Emals: {sayser, eyaaob, psegel, avardy, jwolf}@ucsdedu Abstract
More informationSupplementary Material for Limits on Sparse Support Recovery via Linear Sketching with Random Expander Matrices
Joata Scarlett ad Volka Cever Supplemetary Materal for Lmts o Sparse Support Recovery va Lear Sketcg wt Radom Expader Matrces (AISTATS 26, Joata Scarlett ad Volka Cever) Note tat all ctatos ere are to
More informationJournal of Mathematical Analysis and Applications
J. Math. Aal. Appl. 365 200) 358 362 Cotets lsts avalable at SceceDrect Joural of Mathematcal Aalyss ad Applcatos www.elsever.com/locate/maa Asymptotc behavor of termedate pots the dfferetal mea value
More informationChapter 3 Sampling For Proportions and Percentages
Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys
More informationA NEW LOG-NORMAL DISTRIBUTION
Joural of Statstcs: Advaces Theory ad Applcatos Volume 6, Number, 06, Pages 93-04 Avalable at http://scetfcadvaces.co. DOI: http://dx.do.org/0.864/jsata_700705 A NEW LOG-NORMAL DISTRIBUTION Departmet of
More informationHomework 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 informationChannel Polarization and Polar Codes; Capacity Achieving
Chael Polarzato ad Polar Codes; Capacty chevg Peyma Hesam Tutoral of Iformato Theory Course Uversty of otre Dame December, 9, 009 bstract: ew proposed method for costructg codes that acheves the symmetrc
More informationBounds for the Connective Eccentric Index
It. J. Cotemp. Math. Sceces, Vol. 7, 0, o. 44, 6-66 Bouds for the Coectve Eccetrc Idex Nlaja De Departmet of Basc Scece, Humates ad Socal Scece (Mathematcs Calcutta Isttute of Egeerg ad Maagemet Kolkata,
More informationThe internal structure of natural numbers, one method for the definition of large prime numbers, and a factorization test
Fal verso The teral structure of atural umbers oe method for the defto of large prme umbers ad a factorzato test Emmaul Maousos APM Isttute for the Advacemet of Physcs ad Mathematcs 3 Poulou str. 53 Athes
More informationSolution of General Dual Fuzzy Linear Systems. Using ABS Algorithm
Appled Mathematcal Sceces, Vol 6, 0, o 4, 63-7 Soluto of Geeral Dual Fuzzy Lear Systems Usg ABS Algorthm M A Farborz Aragh * ad M M ossezadeh Departmet of Mathematcs, Islamc Azad Uversty Cetral ehra Brach,
More informationAlmost Sure Convergence of Pair-wise NQD Random Sequence
www.ccseet.org/mas Moder Appled Scece Vol. 4 o. ; December 00 Almost Sure Covergece of Par-wse QD Radom Sequece Yachu Wu College of Scece Gul Uversty of Techology Gul 54004 Cha Tel: 86-37-377-6466 E-mal:
More informationBAYESIAN ESTIMATOR OF A CHANGE POINT IN THE HAZARD FUNCTION
Mathematcal ad Computatoal Applcatos, Vol. 7, No., pp. 29-38, 202 BAYESIAN ESTIMATOR OF A CHANGE POINT IN THE HAZARD FUNCTION Durdu Karasoy Departmet of Statstcs, Hacettepe Uversty, 06800 Beytepe, Akara,
More informationCubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem
Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs
More informationSimulation Output Analysis
Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5
More informationLarge and Moderate Deviation Principles for Kernel Distribution Estimator
Iteratoal Mathematcal Forum, Vol. 9, 2014, o. 18, 871-890 HIKARI Ltd, www.m-hkar.com http://dx.do.org/10.12988/mf.2014.4488 Large ad Moderate Devato Prcples for Kerel Dstrbuto Estmator Yousr Slaou Uversté
More informationBasics of Information Theory: Markku Juntti. Basic concepts and tools 1 Introduction 2 Entropy, relative entropy and mutual information
: Maru Jutt Overvew he propertes of adlmted Gaussa chaels are further studed, parallel Gaussa chaels ad Gaussa chaels wth feedac are solved. Source he materal s maly ased o Sectos.4.6 of the course oo
More informationNP!= P. By Liu Ran. Table of Contents. The P versus NP problem is a major unsolved problem in computer
NP!= P By Lu Ra Table of Cotets. Itroduce 2. Prelmary theorem 3. Proof 4. Expla 5. Cocluso. Itroduce The P versus NP problem s a major usolved problem computer scece. Iformally, t asks whether a computer
More informationChapter 14 Logistic Regression Models
Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as
More informationPr[X (p + t)n] e D KL(p+t p)n.
Cheroff Bouds Wolfgag Mulzer 1 The Geeral Boud Let P 1,..., m ) ad Q q 1,..., q m ) be two dstrbutos o m elemets,.e.,, q 0, for 1,..., m, ad m 1 m 1 q 1. The Kullback-Lebler dvergece or relatve etroy of
More informationComparing Different Estimators of three Parameters for Transmuted Weibull Distribution
Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted
More informationPTAS 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 informationProbabilistic Meanings of Numerical Characteristics for Single Birth Processes
A^VÇÚO 32 ò 5 Ï 206 c 0 Chese Joural of Appled Probablty ad Statstcs Oct 206 Vol 32 No 5 pp 452-462 do: 03969/jss00-426820605002 Probablstc Meags of Numercal Characterstcs for Sgle Brth Processes LIAO
More informationNP!= P. By Liu Ran. Table of Contents. The P vs. NP problem is a major unsolved problem in computer
NP!= P By Lu Ra Table of Cotets. Itroduce 2. Strategy 3. Prelmary theorem 4. Proof 5. Expla 6. Cocluso. Itroduce The P vs. NP problem s a major usolved problem computer scece. Iformally, t asks whether
More informationAlgorithms Theory, Solution for Assignment 2
Juor-Prof. Dr. Robert Elsässer, Marco Muñz, Phllp Hedegger WS 2009/200 Algorthms Theory, Soluto for Assgmet 2 http://lak.formatk.u-freburg.de/lak_teachg/ws09_0/algo090.php Exercse 2. - Fast Fourer Trasform
More information( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model
Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch
More informationInternational Journal of Mathematical Archive-5(8), 2014, Available online through ISSN
Iteratoal Joural of Mathematcal Archve-5(8) 204 25-29 Avalable ole through www.jma.fo ISSN 2229 5046 COMMON FIXED POINT OF GENERALIZED CONTRACTION MAPPING IN FUZZY METRIC SPACES Hamd Mottagh Golsha* ad
More informationDiscrete 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 informationThe 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 informationCHAPTER 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 informationClass 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 informationChapter 4 Multiple Random Variables
Revew o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for Chapter 4-5 Notes: Although all deftos ad theorems troduced our lectures ad ths ote are mportat ad you should be famlar wth, but I put those
More informationParameter, Statistic and Random Samples
Parameter, Statstc ad Radom Samples A parameter s a umber that descrbes the populato. It s a fxed umber, but practce we do ot kow ts value. A statstc s a fucto of the sample data,.e., t s a quatty whose
More informationECE 559: Wireless Communication Project Report Diversity Multiplexing Tradeoff in MIMO Channels with partial CSIT. Hoa Pham
ECE 559: Wreless Commucato Project Report Dversty Multplexg Tradeoff MIMO Chaels wth partal CSIT Hoa Pham. Summary I ths project, I have studed the performace ga of MIMO systems. There are two types of
More informationBeam Warming Second-Order Upwind Method
Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet
More informationSummary 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 informationRademacher Complexity. Examples
Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed
More informationSTRONG CONSISTENCY OF LEAST SQUARES ESTIMATE IN MULTIPLE REGRESSION WHEN THE ERROR VARIANCE IS INFINITE
Statstca Sca 9(1999), 289-296 STRONG CONSISTENCY OF LEAST SQUARES ESTIMATE IN MULTIPLE REGRESSION WHEN THE ERROR VARIANCE IS INFINITE J Mgzhog ad Che Xru GuZhou Natoal College ad Graduate School, Chese
More informationEntropy ISSN by MDPI
Etropy 2003, 5, 233-238 Etropy ISSN 1099-4300 2003 by MDPI www.mdp.org/etropy O the Measure Etropy of Addtve Cellular Automata Hasa Aı Arts ad Sceces Faculty, Departmet of Mathematcs, Harra Uversty; 63100,
More information