國立虎尾科技大學 |

Discrete probability models and methods = probability on graphs and trees, Markov chains and random fields, entropy and coding /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Discrete probability models and methods/ by Pierre Bremaud.
其他題名:	probability on graphs and trees, Markov chains and random fields, entropy and coding /
作者:	Bremaud, Pierre.
出版者:	Cham :Springer International Publishing : : 2017.,
面頁冊數:	xiv, 559 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Probabilities - Data processing. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-43476-6
ISBN:	9783319434766

Discrete probability models and methods = probability on graphs and trees, Markov chains and random fields, entropy and coding /
Bremaud, Pierre.

Discrete probability models and methodsprobability on graphs and trees, Markov chains and random fields, entropy and coding /[electronic resource] :by Pierre Bremaud. - Cham :Springer International Publishing :2017. - xiv, 559 p. :ill., digital ;24 cm. - Probability theory and stochastic modelling,v.782199-3130 ;. - Probability theory and stochastic modelling ;v.72..

Introduction -- 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.

The 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.

ISBN: 9783319434766

Standard No.: 10.1007/978-3-319-43476-6doiSubjects--Topical Terms:

678601
Probabilities
--Data processing.

LC Class. No.: QA273.19.E4

Dewey Class. No.: 519.2

Discrete probability models and methods = probability on graphs and trees, Markov chains and random fields, entropy and coding /
LDR:02565nam a2200337 a 4500 001 958485
003 DE-He213
005 20170815134923.0
006 m d
007 cr nn 008maaau
008 201118s2017 gw s 0 eng d
020 $a 9783319434766 $q (electronic bk.)
020 $a 9783319434759 $q (paper)
024 7 $a 10.1007/978-3-319-43476-6 $2 doi
035 $a 978-3-319-43476-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA273.19.E4
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.2 $2 23
090 $a QA273.19.E4 $b B836 2017
100 1 $a Bremaud, Pierre. $3 1250664
245 1 0 $a Discrete probability models and methods $h [electronic resource] : $b probability on graphs and trees, Markov chains and random fields, entropy and coding / $c by Pierre Bremaud.
260 $a Cham : $c 2017. $b Springer International Publishing : $b Imprint: Springer,
300 $a xiv, 559 p. : $b ill., digital ; $c 24 cm.
490 1 $a Probability theory and stochastic modelling, $x 2199-3130 ; $v v.78
505 0 $a Introduction -- 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.
520 $a The 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.
650 0 $a Probabilities $x Data processing. $3 678601
650 0 $a Computer science $x Mathematics. $3 528496
650 1 4 $a Mathematics. $3 527692
650 2 4 $a Probability Theory and Stochastic Processes. $3 593945
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Graph Theory. $3 786670
650 2 4 $a Coding and Information Theory. $3 669784
650 2 4 $a Computer Communication Networks. $3 669310
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Probability theory and stochastic modelling ; $v v.72. $3 1064457
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-43476-6
950 $a Mathematics and Statistics (Springer-11649)