06440cam a2200901 i 4500 ocn849801365 OCoLC 20171106095517.0 m o d cr ||||||||||| 130620s2013 enk ob 001 0 eng 2013025344 9781118728024 (ePub) 1118728025 (ePub) 9781118728031 (Adobe PDF) 1118728033 (Adobe PDF) 9781118728055 (MobiPocket) 111872805X (MobiPocket) 9781118728048 (electronic bk.) 1118728041 (electronic bk.) 1118357728 (hardback) 9781118357729 (hardback) 1299805329 9781299805323 9781118357729 (hardback) EB00088144 Recorded Books AU@ 000051651237 AU@ 000052905822 AU@ 000053337109 CHBIS 010131686 CHNEW 000633925 CHVBK 314712984 DEBBG BV041634240 DEBBG BV041904726 DEBSZ 397623755 DEBSZ 42317097X DEBSZ 449379965 DKDLA 820120-katalog:000667321 NZ1 15341824 DEBBG BV043396027 CHVBK 40341122X CHNEW 000901240 (OCoLC)849801365 (OCoLC)856626109 (OCoLC)859161262 (OCoLC)862997289 (OCoLC)865020993 511783 MIL DLC eng rda pn DLC YDX N$T CUS YDXCP E7B OCLCF AU@ OCLCQ DG1 OCLCQ COO IDEBK EBLCP MEAUC DEBSZ RECBK DEBBG OCLCQ pcc MAIN QA276.4 MAT 003000 bisacsh MAT 029000 bisacsh 519.501/13 23 MAT029000 bisacsh Voss, Jochen. An introduction to statistical computing : a simulation-based approach / Jochen Voss. [electronic resource] First edition. Chichester, West Sussex, UK : John Wiley & Sons, Inc., 2013. 1 online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier Wiley series in computational statistics Includes bibliographical references and index. An Introduction to Statistical Computing; Contents; List of algorithms; Preface; Nomenclature; 1 Random number generation; 1.1 Pseudo random number generators; 1.1.1 The linear congruential generator; 1.1.2 Quality of pseudo random number generators; 1.1.3 Pseudo random number generators in practice; 1.2 Discrete distributions; 1.3 The inverse transform method; 1.4 Rejection sampling; 1.4.1 Basic rejection sampling; 1.4.2 Envelope rejection sampling; 1.4.3 Conditional distributions; 1.4.4 Geometric interpretation; 1.5 Transformation of random variables; 1.6 Special-purpose methods. 1.7 Summary and further readingExercises; 2 Simulating statistical models; 2.1 Multivariate normal distributions; 2.2 Hierarchical models; 2.3 Markov chains; 2.3.1 Discrete state space; 2.3.2 Continuous state space; 2.4 Poisson processes; 2.5 Summary and further reading; Exercises; 3 Monte Carlo methods; 3.1 Studying models via simulation; 3.2 Monte Carlo estimates; 3.2.1 Computing Monte Carlo estimates; 3.2.2 Monte Carlo error; 3.2.3 Choice of sample size; 3.2.4 Refined error bounds; 3.3 Variance reduction methods; 3.3.1 Importance sampling; 3.3.2 Antithetic variables; 3.3.3 Control variates. 3.4 Applications to statistical inference3.4.1 Point estimators; 3.4.2 Confidence intervals; 3.4.3 Hypothesis tests; 3.5 Summary and further reading; Exercises; 4 Markov Chain Monte Carlo methods; 4.1 The Metropolis-Hastings method; 4.1.1 Continuous state space; 4.1.2 Discrete state space; 4.1.3 Random walk Metropolis sampling; 4.1.4 The independence sampler; 4.1.5 Metropolis-Hastings with different move types; 4.2 Convergence of Markov Chain Monte Carlo methods; 4.2.1 Theoretical results; 4.2.2 Practical considerations; 4.3 Applications to Bayesian inference; 4.4 The Gibbs sampler. 4.4.1 Description of the method4.4.2 Application to parameter estimation; 4.4.3 Applications to image processing; 4.5 Reversible Jump Markov Chain Monte Carlo; 4.5.1 Description of the method; 4.5.2 Bayesian inference for mixture distributions; 4.6 Summary and further reading; 4.6 Exercises; 5 Beyond Monte Carlo; 5.1 Approximate Bayesian Computation; 5.1.1 Basic Approximate Bayesian Computation; 5.1.2 Approximate Bayesian Computation with regression; 5.2 Resampling methods; 5.2.1 Bootstrap estimates; 5.2.2 Applications to statistical inference; 5.3 Summary and further reading; Exercises. 6 Continuous-time models6.1 Time discretisation; 6.2 Brownian motion; 6.2.1 Properties; 6.2.2 Direct simulation; 6.2.3 Interpolation and Brownian bridges; 6.3 Geometric Brownian motion; 6.4 Stochastic differential equations; 6.4.1 Introduction; 6.4.2 Stochastic analysis; 6.4.3 Discretisation schemes; 6.4.4 Discretisation error; 6.5 Monte Carlo estimates; 6.5.1 Basic Monte Carlo; 6.5.2 Variance reduction methods; 6.5.3 Multilevel Monte Carlo estimates; 6.6 Application to option pricing; 6.7 Summary and further reading; Exercises; Appendix A Probability reminders; A.1 Events and probability. "This is a book about exploring random systems using computer simulation and thus, this book combines two different topic areas which have always fascinated me: the mathematical theory of probability and the art of programming computers"-- Provided by publisher. Print version record and CIP data provided by publisher; resource not viewed. Mathematical statistics Data processing. MATHEMATICS Probability & Statistics General. bisacsh Mathematical statistics Data processing. fast (OCoLC)fst01012133 Electronic books. Print version: Voss, Jochen. Introduction to statistical computing. Chichester, West Sussex : Wiley, 2014 9781118357729 (DLC) 2013019321 (OCoLC)841894071 Wiley series in computational statistics. http://onlinelibrary.wiley.com/book/10.1002/9781118728048 Wiley Online Library ddc BK 206840 206840