On the Finite-Length Performance of Universal Coding for k-ary Memoryless Sources
|
|
- Dorcas Bennett
- 5 years ago
- Views:
Transcription
1 Forty-ghth Annual Allerton Conference Allerton House, UIUC, Illnos, USA Septeber 9 - October, 00 On the Fnte-Length Perforance of Unversal Codng for -ary Meoryless Sources Ahad Bera and Faraarz Fer School of lectrcal and Coputer ngneerng Georga Insttute of Technology, Atlanta GA 3033, USA al: bera, fer}@ece.gatech.edu Abstract In ths paper, we nvestgate the perforance of unversal codng schees on fnte-length eoryless sequences. Rssanen deonstrated that for the unversal copresson of -ary eoryless sources, expected redundancy for regular codes s asyptotcally lower bounded by logn for alost all sources. Xe and Barron derved the nax expected redundancy for -ary eoryless sources, whch characterzes the axu redundancy over all possble source paraeters. It does not provde uch nforaton about dfferent source paraeter values. Ths paper s a fnte-length extenson to Rssanen s result. Our treatent n ths paper s probablstc. In partcular, we derve a lower bound on the probablty easure of the sources that are not copressble wth a redundancy saller than a certan fracton of log n. In other words, we deonstrate a lower bound on the redundancy for a gven percentle of sources. We deonstrate that as the length of the eoryless sequence decreases, the redundancy tends to becoe sgnfcant and coparable to the entropy of the sequence. I. INTRODUCTION Recently, the aount of data that s beng stored n storage systes has been ncreasng wth a very hgh rate. Hence, the developent of copresson for storage systes has ganed a lot of nterest. In any cases, one coplete database ay be copressed to less than one tenth of ts orgnal sze. The redundancy n the data ay be leveraged to sgnfcantly reduce the cost of data antenance as well as data transsson. However, ost applcatons requre that ndvdual fles be retreved and updated separate fro the rest of the database. Therefore, the copressed data for ndvdual fles need to be ndependently retrevable and updateable. On the other hand, the ndvdual fles are relatvely very sall. Moreover, the fles ay coe fro varous natures, whch calls for unversal copresson. It s well nown that f a sall fle s copressed alone, exstng unversal copresson technques are unable to acheve good perforance. Rssanen s result descrbes asyptotc achevable copresson rates for unversal copresson of ndvdual fles,. However, t does not provde uch nsght on the perforance of unversal codng for a sall gven fle. The nax redundancy 3 s concerned wth the axu redundancy over all paraeters. However, t does not characterze the redundancy for other paraeters. Snce Shannon s wor on the analyss of councaton systes 4, any researchers have contrbuted towards the developent of source codng schees wth the copresson rate as close as possble to the entropy rate of the nforaton source. It s well nown that usng a prefx-free code, a statonary ergodc nforaton source cannot be copressed at a rate lower than the entropy rate of the source 5. Thus, the goal of source codng s to acheve a copresson rate that approaches the entropy rate. In ths paper, we focus on the unversal copresson of eoryless sources wth a fnte-alphabet sze. Let S denote a unversal eoryless nforaton source wth -ary alphabetα = α,...,α }. Further, denoteθ = θ,...,θ Theta as the vector n the -densonal splex of source paraeters such that for a sybol X generated by S, PX = α = θ, for. Let hθ be the entropy rate of S gven paraeters θ,.e., hθ = j θ j logθ j. Fnally, we use the notaton X n = X,...,X n to present a vector of length n of d rando varables generated by S. Let lx n L denote the regular length functon that descrbes the codeword assocated wth the sequence X n. Denote R n l,θ as the expected redundancy of the code wth length functon l. and paraeter vector θ on a sequence of length n, defned as the dfference between the expected codeword length and the entropy of the sequence X n : R n l,θ = lx n nhθ. For an asyptotcally optal code wth length functon lx n, Rnl,θ n 0 as n for all θ. Provded that the statstcs of the nforaton source are nown, Huffan bloc codng acheves the entropy rate wth a redundancy saller than bt per source sybol 5. However, the assupton of nown source statstcs fals to hold for any practcal applcatons. We usually cannot assue a pror nowledge on the statstcs of the source although we stll wsh to copress the unnown statonary ergodc source to ts entropy rate. Ths s nown as the unversal copresson proble. The axu average redundancy for a length functon of a code wth length functon l s gven as R n l = ax θ Θ R n l,θ, whch s lower bounded by the nax average redundancy R n = n l L ax θ Θ R n l,θ 3. The leadng ter of the average nax redundancy s asyptotcally log n. Accordng to Rssanen s results, for the unversal copresson of eoryless sources wth /0/$ I 740
2 unforly dstrbuted paraeter vector θ, the redundancy of regular codes s asyptotcally lower bounded byr n l,θ δ logn, for all ǫ > 0 and alost all sources. These results were later extended n 6, 7 to ore general classes of sources. The asyptotc lower bound s tght snce there exst codng schees that acheve the bound asyptotcally, 8. We wll forally state Rssanen s result n Sec. II. In ths paper, we study the redundancy for the unversal copresson n fnte-length rege. As the frst step, we consder unversal codng for -ary eoryless sources. The wor can be vewed as the extenson of Rssanen s probablstc treatent to the fnte-length sequences. The rest of ths paper s organzed as follows. In Secton II, we forally state the fnte-length unversal copresson proble followed by our an result. In Secton III, we present the proof of the an result. In Secton IV, we deonstrate the sgnfcance of the an result through two an exaples. Fnally, the concluson s gven n Secton V. II. PROBLM STATMNT AND MAIN RSULTS In ths secton, we forally state the fnte-length redundancy proble and present our an result and ts plcatons. We focus on two-part codes, where the copresson schee utlzes bts for the dentfcaton of an estate for the unnown source paraeters. The estate paraeter s then used for the copresson of the sequence. It has been deonstrated that the two-part assupton brngs about a sall O redundancy ter 9. Ths corresponds to possble estate ponts n the paraeter space for the unnown paraeter. In other words, the copresson schee chooses the best aong estate ponts n the paraeter space as t copresses the nput sequence. Let Φ = φ,...,φ } denote the set of all estate ponts. Note that each φ s a pont n the -densonal splex of θ. For each sequence X n, there s an estate pont β = βx n Φ that nzes the redundancy. Let r denote the nuber of tes sybol α appears n the sequence X n. Let f denote the relatve frequency of sybol α,.e., f = r /n. Let µ θ denote the probablty easure defned over a eoryless source wth paraeter vector θ as µ θ X n = PX n θ = θ r...θr. We requre the length functon l θ X n be regular,.e., l θ X n log µ θ X n X n, 3 where µ θ X n s the eoryless probablty easure defned by the paraeter vectorθ. Note that the requreent 3 s not restrctve snce all codes that we now are regular. Let l β X n denote the length functon nduced by β Φ. Further denote µ β X n as the probablty easure nduced by β: µ β X n = β r...β r. 4 In ths paper logx always denotes the logarth of x n base. Increasng results n an exponental growth n the nuber of estate ponts and ore accurate estate for the unnown source paraeter vector hence better copresson. On the other hand, ncreasng drectly ncreases the copresson overhead. Therefore, t s desrable to fnd the best that nzes the total codeword length as lx n = n +l βx n }. 5 Snce we assued the code s regular, we ay use 3 to bound the redundancy rate } R n θ n +log µ β X n nhθ. 6 Usng 4, we get R n θ n +n } nhθ. 7 = Our goal s to better characterze the lower bound n 7. In, Rssanen proved an asyptotc lower bound on the unversal copresson of paraetrc sources that can be represented wth paraeters. The followng asyptotc lower bound on the redundancy rate of unversal codng of - ary nforaton sources s a drect consequence of Rssanen s result: Theore Let S denote a -ary eoryless nforaton source wth paraeter vectorθ. LetX n denote a sequence of lengthnproduced bys. Let lx n denote any regular length functon for unversal copresson. Then, for all paraeters θ, except n a set of asyptotcally Lebesgue volue zero, we have l n R n θ ǫ, ǫ > 0, 8 logn Whle Theore descrbes the asyptotc fundaental lts of the unversal copresson of eoryless nforaton sources, t does not provde uch nsght for the case of fnte-length n. Moreover, the result excludes an asyptotcally volue zeros set of paraeter vectors θ that has non-zero volue for any fnte n. In 3, Xe and Barron derve the expected nax redundancy R n for eoryless sources, where they deonstrate that R n = n log +log π Γ/ Γ/ +o, 9 where Γx = 0 t x e t dt s the gaa functon. The nax redundancy characterzes the axu redundancy on the space of all possble paraeter vectors but does not ply uch about the rest of the space of the paraeter vectors. In ths paper, we derve a lower bound on the probablty that a source wth paraeter vector θ s copressed wth redundancy rate R n θ > R 0 for any R 0 > 0. In other words, we fnd a lower bound on PR n θ > R 0. Usng ths 74
3 result, we deonstrate the fundaental lts of the unversal copresson for fnte-length n. The followng s our an result: Theore Let S denote a -ary eoryless nforaton source wth paraeter vector θ that follows the Drchlet dstrbuton. Let X n denote a sequence of length n produced by S. Let lx n denote any regular length functon. Let ǫ be a real nuber such that 0 < ǫ <. Then, the probablty that R n θ s greater than ǫ tes the asyptotc bound s lower bounded as follows: P R n θ logn ǫ where B = Γ Γ + B en ǫ, 0 π. Note that B π for. Proof: See Secton III. Note that t s straghtforward to deduce Theore fro Theore by lettng n. Note that for any ǫ such that B > 0, R ǫ = ǫ logn s a lower bound on the nax redundancy snce there exsts a source that has a redundancy of at least R ǫ. We wll later deonstrate that ths lower bound s actually tght, and gves bac the nax redundancy. en ǫ III. PROOF OF TH MAIN RSULT In ths secton, we present the proof of Theore. We brea down the an proof to a few nteredate leas that consttute the an proof. Frst, we splfy 7 as Lea The redundancy rate R n θ s lower bounded by } θ R n θ n +n X n. = Proof: Ths s straghtforward snce hθ = = f log θ. In order to bound the redundancy n Lea, n the followng, we bound = f θ log X n. Note that ths ter plctly depends on snce s a functon of. Lea Assue that β Φ such that ; Dθ β Dθ φ. Then, we have where θ X n Dθ β, = Dx y = x j log j= xj y j. Proof: See Appendx I Note that Dθ s the non-negatve Kullbac Lebler dvergence between the probablty easures defned by θ and β. We use a probablstc treatent n order to bound Dθ β for a certan percentage of source paraeters. We assue that the paraeter vector θ follows the Drchlet dstrbuton. The Drchlet pror dstrbuton s partcularly nterestng snce t results n unfor redundancy over the paraeter vector space, whch results n the acheveent of the nax expected redundancy 3, 0. The Drchlet probablty dstrbuton for the paraeter vector θ s gven by: pθ = Γ/ Γ/ j= θj, 3 where Γ. s the gaa functon defned n Theore. In order to bound the redundancy rate R n θ, n the followng, we fnd an upper bound on the Lebegue easure of the volue defned by Dθ β < δ n the - densonal splex of θ. Snce β Φ, the total easure of the volue defned by φ Φ; Dθ φ < δ ay be upper bounded as well. Ths provdes wth a lower bound on the easure of the sources wth R n θ δ. Lea 3 Let β = β,...,β and θ = θ,...,θ be a fxed and a varable pont n the -densonal splex of θ, respectvely. If θ follows the Drchlet dstrbuton, the probablty PDθ β < δ s upper bounded by PDθ β < δ Γ δ Γ. 4 + Moreover, P φ Φ; Dθ φ < δ s upper bounded by P φ Φ; Dθ φ < δ Γ δ Γ. + 5 Proof: See Appendx II. Lea 3 states that the probablty easure that s covered PDθ β < δ does not depend on β when θ follows the Drchlet pror. In other words, the choce of the set of the paraeter ponts Φ does not affect the perforance of the copresson. We are now equpped to prove the an result gven n Theore. Proof: Proof of Theore Lea 3 bounds the probablty easure of the paraeter vectors that ay be copressed wth a sall redundancy. The condton n Lea s satsfed f < logn for ponts close to soe φ Φ. Ths s because = on and thus the dstance between the ponts s ω/ n. Usng Leas and, R n θ n n +ndθ φ }. 6 Therefore, P R n θ logn ǫ 7 74
4 R PrR n θ,l>r 0 P 0 n = 8 Ths Wor n = 8 Mnax n = 3 Ths Wor n = 3 Mnax n = 8 Ths Wor n = 8 Mnax n = 5 Ths Wor n = 5 Mnax P 0 R PrR n θ,l>r 0 P 0 n = 5b Ths Wor 0 n = 5b Mnax n = b Ths Wor n = b Mnax 0 n = 8b Ths Wor n = 8b Mnax n = 3b Ths Wor n = 3b Mnax P 0 Fg.. Redundancy level R 0 as a functon of percentle of sources P 0 wth R nl,θ > R 0. Alphabet sze: =. P n n = n n P n +ndθ φ } ǫ Dθ φ ǫ Γ Γ + π n logn n } δ loge logn The last nequalty s obtaned usng Lea 3. Here, δ s gven by δ = ǫ n logn n. Carryng out the nzaton n 0 leads to the desred result n Theore. IV. LABORATION OF TH RSULTS AND XAMPLS In ths secton, we llustrate the results through two exaples. In Fgures and, the x-axs s a percentle P 0 and they-axs represents a redundancy levelr 0. The sold curves deonstrate the derved lower bound on the redundancy as a functon of the percentle of the sources that have redundancy beyond the specfed level based on Theore,.e., we have PR n θ R 0 P 0. In other words, at least a fracton P 0 of the sources that are chosen fro the Drchlet pror have an expected redundancy that s greater than R 0. A. Mnax Redundancy of Two-Part Codes: Lower Bound Note that the sold curves are a lower bound on the nax redundancy for all values of P 0 > 0. Ths s due to the fact that a non-zero percentage of the sources ay not be copressed beyond the level R 0 hence the worst case s ncluded n ths regon. Therefore, we ay use our result to derve a lower bound on the nax redundancy of two-part codes when P 0 0 as a sde product: R n,p logn logb log + loge, Fg.. Redundancy level R 0 as a functon of percentle of the sources P 0 wth R nl,θ < R 0. Alphabet sze: = 56. where B s defned n Theore. Ths ay be rewrtten as follows: + R n,p R n +logγ log. 3 e Note that t s straghtforward to deonstrate that the lower bound R n,p R n as,.e., the effect of two-part assupton wll be neglgble. B. xaple : = Frst, for a Bernoull nforaton source,.e., =, we deonstrate the fundaental lts for the copresson of fnte-length sequences. The plots are gven for varous n. The arers n ths Fgure labeled as Mnax deonstrate the nax redundancy for a two-part code. For a Bernoull source, the nax redundancy of the two-part code s gven by 9: R n,p = R n + πe log R n +.048, 4 where R n s the nax redundancy and s defned n 9. As shown n Fgure, at least 40% of Bernoull sequences of length n = 3 n = 8 ay not be copressed beyond a redundancy of bts per sybol. Also, at least 60% of Bernoull sequences of length n = 3 n = 8 ay not be copressed beyond a redundancy of bts per sybol. Note that as n, the redundancy approaches the nax redundancy for ore sources, and asyptotcally redundancy approaches the nax redundancy for all sources as gven n Theore. Also, usng we get the followng lower bound on the nax redundancy of two-part codes for = : R n,p logn+ log πe whch s ndeed equal to the nax redundancy., 5 743
5 C. xaple : = 56 Next, we consder = 56, whch s a coon practce to use the byte as a sybol. In Fgure, the achevable redundancy s deonstrated for four dfferent values of n. Here, the redundancy s easured n bts per source sybol byte. We observe that for n = 5b, we have R n l,θ 300 bts/sybol for alost all sources. Ths s consdered sgnfcant specally for sources wth sall entropy rate. For exaple, f the entropy rate s around bt/sybol, there wll be alost always ore than 50% redundancy n the copresson of a sequence of length n = 5b. We also observe that the two-part assupton does not ncur further redundancy for large. V. CONCLUSION In ths paper, we nvestgated the redundancy rate of unversal codng schees on eoryless nput sequences n the fnte-length rege. We derved a lower bound on the probablty easure of nforaton sources that ay not be copressed beyond any certan redundancy level. Our result ay be vewed as the fnte-length extenson of the prevous asyptotc results. Our result ay be used to evaluate the perforance of unversal source codng on fnte-length sequences. We observe that redundancy ay be very sgnfcant n the copresson of fnte-length lowentropy nforaton sources. Proof: = n! r,...,r j rj=n = APPNDIX I PROOF OF LMMA = r!...r! θr...θr θ X n 6 r n log θ X n, 7 n! s,...,s s!...s! θs...θs θ θ log j sj=n X n, 8 where s = r and s j = r j, j. Note that ths could now be vewed as tang expectatons over a dfferent set of varables. Thus, 8 could be rewrtten as θ log = θ X n. 9 Ths ay be lower bounded usng the fact φ Φ, Dθ φ Dθ β, whch proves the cla. APPNDIX II PROOF OF LMMA 3 Proof: Let fθ = Dθ β = = θ log usng Taylor expanson, we get θ. Then, where Lθ,β = loge = θ. Note that Lθ,β δ, where δ > 0, defnes an ellpsod on the -densonal splex of θ. It s straghtforward to deonstrate that the volue of the ellpsod s gven by δ V β = C loge β, 3 = where C = Γ/ Γ+/ s the volue of the - densonal unt ball. Moreover, snce θ follows the Drchlet dstrbuton, the probablty easure of the covered ellpsod s gven by PLθ,β δ = V β Γ/ Γ/ β j= = Γ δ Γ. 3 + Snce Dθ β Lθ,β, the volue defned by Dθ β < δ s equal to the volue Lθ,β < δ, whch copletes the proof of the frst cla. Although the volue of the ellpsod depends on the pont n the paraeter space, the volue of the ellpsod defned by Dθ β < δ does not depend on β. For the second cla, there are choces for φ., Dθ φ defnes a easure that has the sae upper bound. Thus, the second cla follows drectly fro the frst cla usng the unon bound. RFRNCS J. Rssanen, Unversal codng, nforaton, predcton, and estaton, Inforaton Theory, I Transactons on, vol. 30, no. 4, pp , Jul 984., Coplexty of strngs n the class of Marov sources, Inforaton Theory, I Transactons on, vol. 3, no. 4, pp , Jul Q. Xe and A. Barron, Mnax redundancy for the class of eoryless sources, Inforaton Theory, I Transactons on, vol. 43, no., pp , ar C.. Shannon, A Matheatcal Theory of Councaton, The Bell Syste Techncal Journal, vol. 7, pp , , Jul, Oct T. M. Cover and J. A. Thoas, leents of Inforaton Theory. John Wley and sons, N. Merhav and M. Feder, The nax redundancy s a lower bound for ost sources, n Data Copresson Conference, 994. DCC 94. Proceedngs, , pp M. Feder and N. Merhav, Herarchcal unversal codng, Inforaton Theory, I Transactons on, vol. 4, no. 5, pp , sep F. Wlles, Y. Shtarov, and T. Tjalens, The context-tree weghtng ethod: basc propertes, Inforaton Theory, I Transactons on, vol. 4, no. 3, pp , May P. D. Grunwald, The nu descrpton length prncple. The MIT Press, R.. Krchevsy and V. K. Trofov, The perforance of unversal encodng, Inforaton Theory, I Transactons on, vol. 7, no., pp , March 98. Dθ β Lθ,β+Oθ 3,
Excess Error, Approximation Error, and Estimation Error
E0 370 Statstcal Learnng Theory Lecture 10 Sep 15, 011 Excess Error, Approxaton Error, and Estaton Error Lecturer: Shvan Agarwal Scrbe: Shvan Agarwal 1 Introducton So far, we have consdered the fnte saple
More informationFinding Dense Subgraphs in G(n, 1/2)
Fndng Dense Subgraphs n Gn, 1/ Atsh Das Sarma 1, Amt Deshpande, and Rav Kannan 1 Georga Insttute of Technology,atsh@cc.gatech.edu Mcrosoft Research-Bangalore,amtdesh,annan@mcrosoft.com Abstract. Fndng
More informationSystem in Weibull Distribution
Internatonal Matheatcal Foru 4 9 no. 9 94-95 Relablty Equvalence Factors of a Seres-Parallel Syste n Webull Dstrbuton M. A. El-Dacese Matheatcs Departent Faculty of Scence Tanta Unversty Tanta Egypt eldacese@yahoo.co
More information1 Definition of Rademacher Complexity
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #9 Scrbe: Josh Chen March 5, 2013 We ve spent the past few classes provng bounds on the generalzaton error of PAClearnng algorths for the
More informationXII.3 The EM (Expectation-Maximization) Algorithm
XII.3 The EM (Expectaton-Maxzaton) Algorth Toshnor Munaata 3/7/06 The EM algorth s a technque to deal wth varous types of ncoplete data or hdden varables. It can be appled to a wde range of learnng probles
More informationCOS 511: Theoretical Machine Learning
COS 5: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture #0 Scrbe: José Sões Ferrera March 06, 203 In the last lecture the concept of Radeacher coplexty was ntroduced, wth the goal of showng that
More informationBAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS. Dariusz Biskup
BAYESIAN CURVE FITTING USING PIECEWISE POLYNOMIALS Darusz Bskup 1. Introducton The paper presents a nonparaetrc procedure for estaton of an unknown functon f n the regresson odel y = f x + ε = N. (1) (
More informationOn the Construction of Polar Codes
On the Constructon of Polar Codes Ratn Pedarsan School of Coputer and Councaton Systes, Lausanne, Swtzerland. ratn.pedarsan@epfl.ch S. Haed Hassan School of Coputer and Councaton Systes, Lausanne, Swtzerland.
More informationTwo Conjectures About Recency Rank Encoding
Internatonal Journal of Matheatcs and Coputer Scence, 0(205, no. 2, 75 84 M CS Two Conjectures About Recency Rank Encodng Chrs Buhse, Peter Johnson, Wlla Lnz 2, Matthew Spson 3 Departent of Matheatcs and
More informationFermi-Dirac statistics
UCC/Physcs/MK/EM/October 8, 205 Fer-Drac statstcs Fer-Drac dstrbuton Matter partcles that are eleentary ostly have a type of angular oentu called spn. hese partcles are known to have a agnetc oent whch
More informationComputational and Statistical Learning theory Assignment 4
Coputatonal and Statstcal Learnng theory Assgnent 4 Due: March 2nd Eal solutons to : karthk at ttc dot edu Notatons/Defntons Recall the defnton of saple based Radeacher coplexty : [ ] R S F) := E ɛ {±}
More information1 Review From Last Time
COS 5: Foundatons of Machne Learnng Rob Schapre Lecture #8 Scrbe: Monrul I Sharf Aprl 0, 2003 Revew Fro Last Te Last te, we were talkng about how to odel dstrbutons, and we had ths setup: Gven - exaples
More informationOn the Construction of Polar Codes
On the Constructon of Polar Codes Ratn Pedarsan School of Coputer and Councaton Systes, Lausanne, Swtzerland. ratn.pedarsan@epfl.ch S. Haed Hassan School of Coputer and Councaton Systes, Lausanne, Swtzerland.
More informationCentroid Uncertainty Bounds for Interval Type-2 Fuzzy Sets: Forward and Inverse Problems
Centrod Uncertanty Bounds for Interval Type-2 Fuzzy Sets: Forward and Inverse Probles Jerry M. Mendel and Hongwe Wu Sgnal and Iage Processng Insttute Departent of Electrcal Engneerng Unversty of Southern
More informationXiangwen Li. March 8th and March 13th, 2001
CS49I Approxaton Algorths The Vertex-Cover Proble Lecture Notes Xangwen L March 8th and March 3th, 00 Absolute Approxaton Gven an optzaton proble P, an algorth A s an approxaton algorth for P f, for an
More informationWhat is LP? LP is an optimization technique that allocates limited resources among competing activities in the best possible manner.
(C) 998 Gerald B Sheblé, all rghts reserved Lnear Prograng Introducton Contents I. What s LP? II. LP Theor III. The Splex Method IV. Refneents to the Splex Method What s LP? LP s an optzaton technque that
More informationThe Expectation-Maximization Algorithm
The Expectaton-Maxmaton Algorthm Charles Elan elan@cs.ucsd.edu November 16, 2007 Ths chapter explans the EM algorthm at multple levels of generalty. Secton 1 gves the standard hgh-level verson of the algorthm.
More informationLecture 3: Shannon s Theorem
CSE 533: Error-Correctng Codes (Autumn 006 Lecture 3: Shannon s Theorem October 9, 006 Lecturer: Venkatesan Guruswam Scrbe: Wdad Machmouch 1 Communcaton Model The communcaton model we are usng conssts
More informationApplied Mathematics Letters
Appled Matheatcs Letters 2 (2) 46 5 Contents lsts avalable at ScenceDrect Appled Matheatcs Letters journal hoepage: wwwelseverco/locate/al Calculaton of coeffcents of a cardnal B-splne Gradr V Mlovanovć
More informationModule 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:
More informationPROBABILITY AND STATISTICS Vol. III - Analysis of Variance and Analysis of Covariance - V. Nollau ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE
ANALYSIS OF VARIANCE AND ANALYSIS OF COVARIANCE V. Nollau Insttute of Matheatcal Stochastcs, Techncal Unversty of Dresden, Gerany Keywords: Analyss of varance, least squares ethod, odels wth fxed effects,
More informationUniversal communication part II: channels with memory
Unversal councaton part II: channels wth eory Yuval Lontz, Mer Feder Tel Avv Unversty, Dept. of EE-Systes Eal: {yuvall,er@eng.tau.ac.l arxv:202.047v2 [cs.it] 20 Mar 203 Abstract Consder councaton over
More informationLeast Squares Fitting of Data
Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2014. All Rghts Reserved. Created: July 15, 1999 Last Modfed: February 9, 2008 Contents 1 Lnear Fttng
More informationOn the number of regions in an m-dimensional space cut by n hyperplanes
6 On the nuber of regons n an -densonal space cut by n hyperplanes Chungwu Ho and Seth Zeran Abstract In ths note we provde a unfor approach for the nuber of bounded regons cut by n hyperplanes n general
More informationarxiv: v2 [math.co] 3 Sep 2017
On the Approxate Asyptotc Statstcal Independence of the Peranents of 0- Matrces arxv:705.0868v2 ath.co 3 Sep 207 Paul Federbush Departent of Matheatcs Unversty of Mchgan Ann Arbor, MI, 4809-043 Septeber
More informationLecture Notes on Linear Regression
Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume
More informationMore metrics on cartesian products
More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of
More informationChapter 13. Gas Mixtures. Study Guide in PowerPoint. Thermodynamics: An Engineering Approach, 5th edition by Yunus A. Çengel and Michael A.
Chapter 3 Gas Mxtures Study Gude n PowerPont to accopany Therodynacs: An Engneerng Approach, 5th edton by Yunus A. Çengel and Mchael A. Boles The dscussons n ths chapter are restrcted to nonreactve deal-gas
More informationChapter 12 Lyes KADEM [Thermodynamics II] 2007
Chapter 2 Lyes KDEM [Therodynacs II] 2007 Gas Mxtures In ths chapter we wll develop ethods for deternng therodynac propertes of a xture n order to apply the frst law to systes nvolvng xtures. Ths wll be
More informationChapter One Mixture of Ideal Gases
herodynacs II AA Chapter One Mxture of Ideal Gases. Coposton of a Gas Mxture: Mass and Mole Fractons o deterne the propertes of a xture, we need to now the coposton of the xture as well as the propertes
More informationLeast Squares Fitting of Data
Least Squares Fttng of Data Davd Eberly Geoetrc Tools, LLC http://www.geoetrctools.co/ Copyrght c 1998-2015. All Rghts Reserved. Created: July 15, 1999 Last Modfed: January 5, 2015 Contents 1 Lnear Fttng
More informationSeveral generation methods of multinomial distributed random number Tian Lei 1, a,linxihe 1,b,Zhigang Zhang 1,c
Internatonal Conference on Appled Scence and Engneerng Innovaton (ASEI 205) Several generaton ethods of ultnoal dstrbuted rando nuber Tan Le, a,lnhe,b,zhgang Zhang,c School of Matheatcs and Physcs, USTB,
More informationThe Parity of the Number of Irreducible Factors for Some Pentanomials
The Party of the Nuber of Irreducble Factors for Soe Pentanoals Wolfra Koepf 1, Ryul K 1 Departent of Matheatcs Unversty of Kassel, Kassel, F. R. Gerany Faculty of Matheatcs and Mechancs K Il Sung Unversty,
More information3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X
Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number
More informationLECTURE :FACTOR ANALYSIS
LCUR :FACOR ANALYSIS Rta Osadchy Based on Lecture Notes by A. Ng Motvaton Dstrbuton coes fro MoG Have suffcent aount of data: >>n denson Use M to ft Mture of Gaussans nu. of tranng ponts If
More informationError Probability for M Signals
Chapter 3 rror Probablty for M Sgnals In ths chapter we dscuss the error probablty n decdng whch of M sgnals was transmtted over an arbtrary channel. We assume the sgnals are represented by a set of orthonormal
More informationPARAMETER ESTIMATION IN WEIBULL DISTRIBUTION ON PROGRESSIVELY TYPE- II CENSORED SAMPLE WITH BETA-BINOMIAL REMOVALS
Econoy & Busness ISSN 1314-7242, Volue 10, 2016 PARAMETER ESTIMATION IN WEIBULL DISTRIBUTION ON PROGRESSIVELY TYPE- II CENSORED SAMPLE WITH BETA-BINOMIAL REMOVALS Ilhan Usta, Hanef Gezer Departent of Statstcs,
More informationDesigning Fuzzy Time Series Model Using Generalized Wang s Method and Its application to Forecasting Interest Rate of Bank Indonesia Certificate
The Frst Internatonal Senar on Scence and Technology, Islac Unversty of Indonesa, 4-5 January 009. Desgnng Fuzzy Te Seres odel Usng Generalzed Wang s ethod and Its applcaton to Forecastng Interest Rate
More informationMultipoint Analysis for Sibling Pairs. Biostatistics 666 Lecture 18
Multpont Analyss for Sblng ars Bostatstcs 666 Lecture 8 revously Lnkage analyss wth pars of ndvduals Non-paraetrc BS Methods Maxu Lkelhood BD Based Method ossble Trangle Constrant AS Methods Covered So
More informationOn Syndrome Decoding of Punctured Reed-Solomon and Gabidulin Codes 1
Ffteenth Internatonal Workshop on Algebrac and Cobnatoral Codng Theory June 18-24, 2016, Albena, Bulgara pp. 35 40 On Syndroe Decodng of Punctured Reed-Soloon and Gabduln Codes 1 Hannes Bartz hannes.bartz@tu.de
More informationElastic Collisions. Definition: two point masses on which no external forces act collide without losing any energy.
Elastc Collsons Defnton: to pont asses on hch no external forces act collde thout losng any energy v Prerequstes: θ θ collsons n one denson conservaton of oentu and energy occurs frequently n everyday
More informationPreference and Demand Examples
Dvson of the Huantes and Socal Scences Preference and Deand Exaples KC Border October, 2002 Revsed Noveber 206 These notes show how to use the Lagrange Karush Kuhn Tucker ultpler theores to solve the proble
More informationEconomics 101. Lecture 4 - Equilibrium and Efficiency
Economcs 0 Lecture 4 - Equlbrum and Effcency Intro As dscussed n the prevous lecture, we wll now move from an envronment where we looed at consumers mang decsons n solaton to analyzng economes full of
More informationReliability estimation in Pareto-I distribution based on progressively type II censored sample with binomial removals
Journal of Scentfc esearch Developent (): 08-3 05 Avalable onlne at wwwjsradorg ISSN 5-7569 05 JSAD elablty estaton n Pareto-I dstrbuton based on progressvely type II censored saple wth bnoal reovals Ilhan
More informationOur focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e.
SSTEM MODELLIN In order to solve a control syste proble, the descrptons of the syste and ts coponents ust be put nto a for sutable for analyss and evaluaton. The followng ethods can be used to odel physcal
More informationLinear Approximation with Regularization and Moving Least Squares
Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...
More informationDetermination of the Confidence Level of PSD Estimation with Given D.O.F. Based on WELCH Algorithm
Internatonal Conference on Inforaton Technology and Manageent Innovaton (ICITMI 05) Deternaton of the Confdence Level of PSD Estaton wth Gven D.O.F. Based on WELCH Algorth Xue-wang Zhu, *, S-jan Zhang
More informationOn Pfaff s solution of the Pfaff problem
Zur Pfaff scen Lösung des Pfaff scen Probles Mat. Ann. 7 (880) 53-530. On Pfaff s soluton of te Pfaff proble By A. MAYER n Lepzg Translated by D. H. Delpenc Te way tat Pfaff adopted for te ntegraton of
More informationOne-Shot Quantum Information Theory I: Entropic Quantities. Nilanjana Datta University of Cambridge,U.K.
One-Shot Quantu Inforaton Theory I: Entropc Quanttes Nlanjana Datta Unversty of Cabrdge,U.K. In Quantu nforaton theory, ntally one evaluated: optal rates of nfo-processng tasks, e.g., data copresson, transsson
More informationScattering by a perfectly conducting infinite cylinder
Scatterng by a perfectly conductng nfnte cylnder Reeber that ths s the full soluton everywhere. We are actually nterested n the scatterng n the far feld lt. We agan use the asyptotc relatonshp exp exp
More informationLinear Regression Analysis: Terminology and Notation
ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented
More informationLecture 12: Discrete Laplacian
Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly
More informationMATH 829: Introduction to Data Mining and Analysis The EM algorithm (part 2)
1/16 MATH 829: Introducton to Data Mnng and Analyss The EM algorthm (part 2) Domnque Gullot Departments of Mathematcal Scences Unversty of Delaware Aprl 20, 2016 Recall 2/16 We are gven ndependent observatons
More informationJoint Statistical Meetings - Biopharmaceutical Section
Iteratve Ch-Square Test for Equvalence of Multple Treatment Groups Te-Hua Ng*, U.S. Food and Drug Admnstraton 1401 Rockvlle Pke, #200S, HFM-217, Rockvlle, MD 20852-1448 Key Words: Equvalence Testng; Actve
More informationAN ANALYSIS OF A FRACTAL KINETICS CURVE OF SAVAGEAU
AN ANALYI OF A FRACTAL KINETIC CURE OF AAGEAU by John Maloney and Jack Hedel Departent of Matheatcs Unversty of Nebraska at Oaha Oaha, Nebraska 688 Eal addresses: aloney@unoaha.edu, jhedel@unoaha.edu Runnng
More informationPsychology 282 Lecture #24 Outline Regression Diagnostics: Outliers
Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.
More informationDenote the function derivatives f(x) in given points. x a b. Using relationships (1.2), polynomials (1.1) are written in the form
SET OF METHODS FO SOUTION THE AUHY POBEM FO STIFF SYSTEMS OF ODINAY DIFFEENTIA EUATIONS AF atypov and YuV Nulchev Insttute of Theoretcal and Appled Mechancs SB AS 639 Novosbrs ussa Introducton A constructon
More informationx = , so that calculated
Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to
More informationP exp(tx) = 1 + t 2k M 2k. k N
1. Subgaussan tals Defnton. Say that a random varable X has a subgaussan dstrbuton wth scale factor σ< f P exp(tx) exp(σ 2 t 2 /2) for all real t. For example, f X s dstrbuted N(,σ 2 ) then t s subgaussan.
More information1. Statement of the problem
Volue 14, 010 15 ON THE ITERATIVE SOUTION OF A SYSTEM OF DISCRETE TIMOSHENKO EQUATIONS Peradze J. and Tsklaur Z. I. Javakhshvl Tbls State Uversty,, Uversty St., Tbls 0186, Georga Georgan Techcal Uversty,
More informationOutline. Prior Information and Subjective Probability. Subjective Probability. The Histogram Approach. Subjective Determination of the Prior Density
Outlne Pror Inforaton and Subjectve Probablty u89603 1 Subjectve Probablty Subjectve Deternaton of the Pror Densty Nonnforatve Prors Maxu Entropy Prors Usng the Margnal Dstrbuton to Deterne the Pror Herarchcal
More informationInternational Journal of Mathematical Archive-9(3), 2018, Available online through ISSN
Internatonal Journal of Matheatcal Archve-9(3), 208, 20-24 Avalable onlne through www.ja.nfo ISSN 2229 5046 CONSTRUCTION OF BALANCED INCOMPLETE BLOCK DESIGNS T. SHEKAR GOUD, JAGAN MOHAN RAO M AND N.CH.
More informationLecture Space-Bounded Derandomization
Notes on Complexty Theory Last updated: October, 2008 Jonathan Katz Lecture Space-Bounded Derandomzaton 1 Space-Bounded Derandomzaton We now dscuss derandomzaton of space-bounded algorthms. Here non-trval
More informationELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM
ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look
More informationLecture 3. Ax x i a i. i i
18.409 The Behavor of Algorthms n Practce 2/14/2 Lecturer: Dan Spelman Lecture 3 Scrbe: Arvnd Sankar 1 Largest sngular value In order to bound the condton number, we need an upper bound on the largest
More informationGlobal Sensitivity. Tuesday 20 th February, 2018
Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values
More informationParametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010
Parametrc fractonal mputaton for mssng data analyss Jae Kwang Km Survey Workng Group Semnar March 29, 2010 1 Outlne Introducton Proposed method Fractonal mputaton Approxmaton Varance estmaton Multple mputaton
More informationConvergence of random processes
DS-GA 12 Lecture notes 6 Fall 216 Convergence of random processes 1 Introducton In these notes we study convergence of dscrete random processes. Ths allows to characterze phenomena such as the law of large
More informationA Note on Bound for Jensen-Shannon Divergence by Jeffreys
OPEN ACCESS Conference Proceedngs Paper Entropy www.scforum.net/conference/ecea- A Note on Bound for Jensen-Shannon Dvergence by Jeffreys Takuya Yamano, * Department of Mathematcs and Physcs, Faculty of
More informationIntroducing Entropy Distributions
Graubner, Schdt & Proske: Proceedngs of the 6 th Internatonal Probablstc Workshop, Darstadt 8 Introducng Entropy Dstrbutons Noel van Erp & Peter van Gelder Structural Hydraulc Engneerng and Probablstc
More informationAn Optimal Bound for Sum of Square Roots of Special Type of Integers
The Sxth Internatonal Syposu on Operatons Research and Its Applcatons ISORA 06 Xnang, Chna, August 8 12, 2006 Copyrght 2006 ORSC & APORC pp. 206 211 An Optal Bound for Su of Square Roots of Specal Type
More informationITERATIVE ESTIMATION PROCEDURE FOR GEOSTATISTICAL REGRESSION AND GEOSTATISTICAL KRIGING
ESE 5 ITERATIVE ESTIMATION PROCEDURE FOR GEOSTATISTICAL REGRESSION AND GEOSTATISTICAL KRIGING Gven a geostatstcal regresson odel: k Y () s x () s () s x () s () s, s R wth () unknown () E[ ( s)], s R ()
More informationSTAT 3008 Applied Regression Analysis
STAT 3008 Appled Regresson Analyss Tutoral : Smple Lnear Regresson LAI Chun He Department of Statstcs, The Chnese Unversty of Hong Kong 1 Model Assumpton To quantfy the relatonshp between two factors,
More informationVQ widely used in coding speech, image, and video
at Scalar quantzers are specal cases of vector quantzers (VQ): they are constraned to look at one sample at a tme (memoryless) VQ does not have such constrant better RD perfomance expected Source codng
More informationBOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS. M. Krishna Reddy, B. Naveen Kumar and Y. Ramu
BOOTSTRAP METHOD FOR TESTING OF EQUALITY OF SEVERAL MEANS M. Krshna Reddy, B. Naveen Kumar and Y. Ramu Department of Statstcs, Osmana Unversty, Hyderabad -500 007, Inda. nanbyrozu@gmal.com, ramu0@gmal.com
More information,..., k N. , k 2. ,..., k i. The derivative with respect to temperature T is calculated by using the chain rule: & ( (5) dj j dt = "J j. k i.
Suppleentary Materal Dervaton of Eq. 1a. Assue j s a functon of the rate constants for the N coponent reactons: j j (k 1,,..., k,..., k N ( The dervatve wth respect to teperature T s calculated by usng
More informationEigenvalues of Random Graphs
Spectral Graph Theory Lecture 2 Egenvalues of Random Graphs Danel A. Spelman November 4, 202 2. Introducton In ths lecture, we consder a random graph on n vertces n whch each edge s chosen to be n the
More informationLectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix
Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could
More informationSupplementary material: Margin based PU Learning. Matrix Concentration Inequalities
Supplementary materal: Margn based PU Learnng We gve the complete proofs of Theorem and n Secton We frst ntroduce the well-known concentraton nequalty, so the covarance estmator can be bounded Then we
More informationNotes on Frequency Estimation in Data Streams
Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to
More informationEntropy Coding. A complete entropy codec, which is an encoder/decoder. pair, consists of the process of encoding or
Sgnal Compresson Sgnal Compresson Entropy Codng Entropy codng s also known as zero-error codng, data compresson or lossless compresson. Entropy codng s wdely used n vrtually all popular nternatonal multmeda
More informationQuantum and Classical Information Theory with Disentropy
Quantum and Classcal Informaton Theory wth Dsentropy R V Ramos rubensramos@ufcbr Lab of Quantum Informaton Technology, Department of Telenformatc Engneerng Federal Unversty of Ceara - DETI/UFC, CP 6007
More informationLecture 17 : Stochastic Processes II
: Stochastc Processes II 1 Contnuous-tme stochastc process So far we have studed dscrete-tme stochastc processes. We studed the concept of Makov chans and martngales, tme seres analyss, and regresson analyss
More informationDifferentiating Gaussian Processes
Dfferentatng Gaussan Processes Andrew McHutchon Aprl 17, 013 1 Frst Order Dervatve of the Posteror Mean The posteror mean of a GP s gven by, f = x, X KX, X 1 y x, X α 1 Only the x, X term depends on the
More informationHomework Assignment 3 Due in class, Thursday October 15
Homework Assgnment 3 Due n class, Thursday October 15 SDS 383C Statstcal Modelng I 1 Rdge regresson and Lasso 1. Get the Prostrate cancer data from http://statweb.stanford.edu/~tbs/elemstatlearn/ datasets/prostate.data.
More informationGadjah Mada University, Indonesia. Yogyakarta State University, Indonesia Karangmalang Yogyakarta 55281
Reducng Fuzzy Relatons of Fuzzy Te Seres odel Usng QR Factorzaton ethod and Its Applcaton to Forecastng Interest Rate of Bank Indonesa Certfcate Agus aan Abad Subanar Wdodo 3 Sasubar Saleh 4 Ph.D Student
More informationStanford University CS254: Computational Complexity Notes 7 Luca Trevisan January 29, Notes for Lecture 7
Stanford Unversty CS54: Computatonal Complexty Notes 7 Luca Trevsan January 9, 014 Notes for Lecture 7 1 Approxmate Countng wt an N oracle We complete te proof of te followng result: Teorem 1 For every
More informationE Tail Inequalities. E.1 Markov s Inequality. Non-Lecture E: Tail Inequalities
Algorthms Non-Lecture E: Tal Inequaltes If you hold a cat by the tal you learn thngs you cannot learn any other way. Mar Twan E Tal Inequaltes The smple recursve structure of sp lsts made t relatvely easy
More informationKernel Methods and SVMs Extension
Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general
More informationLecture 10 Support Vector Machines II
Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed
More informationVapnik-Chervonenkis theory
Vapnk-Chervonenks theory Rs Kondor June 13, 2008 For the purposes of ths lecture, we restrct ourselves to the bnary supervsed batch learnng settng. We assume that we have an nput space X, and an unknown
More informationQuantum Particle Motion in Physical Space
Adv. Studes Theor. Phys., Vol. 8, 014, no. 1, 7-34 HIKARI Ltd, www.-hkar.co http://dx.do.org/10.1988/astp.014.311136 Quantu Partcle Moton n Physcal Space A. Yu. Saarn Dept. of Physcs, Saara State Techncal
More informationModeling of Dynamic Systems
Modelng of Dynamc Systems Ref: Control System Engneerng Norman Nse : Chapters & 3 Chapter objectves : Revew the Laplace transform Learn how to fnd a mathematcal model, called a transfer functon Learn how
More informationPHYS 1443 Section 002 Lecture #20
PHYS 1443 Secton 002 Lecture #20 Dr. Jae Condtons for Equlbru & Mechancal Equlbru How to Solve Equlbru Probles? A ew Exaples of Mechancal Equlbru Elastc Propertes of Solds Densty and Specfc Gravty lud
More informationCHAPTER 14 GENERAL PERTURBATION THEORY
CHAPTER 4 GENERAL PERTURBATION THEORY 4 Introducton A partcle n orbt around a pont mass or a sphercally symmetrc mass dstrbuton s movng n a gravtatonal potental of the form GM / r In ths potental t moves
More informationSeries Expansion for L p Hardy Inequalities
Seres Expanson for L p Hardy Inequaltes G. BARBATIS, S.FILIPPAS, & A. TERTIKAS ABSTRACT. We consder a general class of sharp L p Hardy nequaltes n R N nvolvng dstance fro a surface of general codenson
More informationSolutions for Homework #9
Solutons for Hoewor #9 PROBEM. (P. 3 on page 379 n the note) Consder a sprng ounted rgd bar of total ass and length, to whch an addtonal ass s luped at the rghtost end. he syste has no dapng. Fnd the natural
More informationPulse Coded Modulation
Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal
More informationFinal Exam Solutions, 1998
58.439 Fnal Exa Solutons, 1998 roble 1 art a: Equlbru eans that the therodynac potental of a consttuent s the sae everywhere n a syste. An exaple s the Nernst potental. If the potental across a ebrane
More information2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification
E395 - Pattern Recognton Solutons to Introducton to Pattern Recognton, Chapter : Bayesan pattern classfcaton Preface Ths document s a soluton manual for selected exercses from Introducton to Pattern Recognton
More information