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035   $a(OCoLC)972330747
035   $a(PPN)198341482
035   $a(EXLCZ)993710000001041181
100   $a20170130d2017      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDiscrete Probability Models and Methods $eProbability on Graphs and Trees, Markov Chains and Random Fields, Entropy and Coding /$fby Pierre Brémaud
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XIV, 559 p. 92 illus.) 
225 1 $aProbability Theory and Stochastic Modelling,$x2199-3149 ;$v78
311   $a3-319-43475-6 
320   $aIncludes bibliographical references and index.
327   $aIntroduction -- 1.Events and probability -- 2.Random variables -- 3.Bounds and inequalities -- 4.Almost-sure convergence -- 5.Coupling and the variation distance -- 6.The probabilistic method -- 7.Codes and trees -- 8.Markov chains -- 9.Branching trees -- 10.Markov fields on graphs -- 11.Random graphs -- 12.Recurrence of Markov chains -- 13.Random walks on graphs -- 14.Asymptotic behaviour of Markov chains -- 15.Monte Carlo sampling -- 16. Convergence rates -- Appendix -- Bibliography.
330   $aThe emphasis in this book is placed on general models (Markov chains, random fields, random graphs), universal methods (the probabilistic method, the coupling method, the Stein-Chen method, martingale methods, the method of types) and versatile tools (Chernoff's bound, Hoeffding's inequality, Holley's inequality) whose domain of application extends far beyond the present text. Although the examples treated in the book relate to the possible applications, in the communication and computing sciences, in operations research and in physics, this book is in the first instance concerned with theory. The level of the book is that of a beginning graduate course. It is self-contained, the prerequisites consisting merely of basic calculus (series) and basic linear algebra (matrices). The reader is not assumed to be trained in probability since the first chapters give in considerable detail the background necessary to understand the rest of the book. .
410  0$aProbability Theory and Stochastic Modelling,$x2199-3149 ;$v78
606   $aProbabilities
606   $aComputer science$xMathematics
606   $aMathematical statistics
606   $aGraph theory
606   $aCoding theory
606   $aInformation theory
606   $aComputer networks
606   $aProbability Theory
606   $aProbability and Statistics in Computer Science
606   $aGraph Theory
606   $aCoding and Information Theory
606   $aComputer Communication Networks
615  0$aProbabilities.
615  0$aComputer science$xMathematics.
615  0$aMathematical statistics.
615  0$aGraph theory.
615  0$aCoding theory.
615  0$aInformation theory.
615  0$aComputer networks.
615 14$aProbability Theory.
615 24$aProbability and Statistics in Computer Science.
615 24$aGraph Theory.
615 24$aCoding and Information Theory.
615 24$aComputer Communication Networks.
676   $a519.2
700   $aBrémaud$b Pierre$4aut$4http://id.loc.gov/vocabulary/relators/aut$056619
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254294203321
996   $aDiscrete probability models and methods$91560490
997   $aUNINA