An introduction to statistical computing : a simulation-based approach /

An introduction to statistical computing : a simulation-based approach /

"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"--

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Voss, Jochen
Μορφή: Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Chichester, West Sussex, UK : John Wiley & Sons, Inc., 2013.
Έκδοση:First edition.
Σειρά:Wiley series in computational statistics.
Θέματα:
Mathematical statistics > Data processing.
MATHEMATICS > Probability & Statistics > General.
Electronic books.
Διαθέσιμο Online:Full Text via HEAL-Link
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049 |a MAIN 
100 1 |a Voss, Jochen. 
245 1 3 |a An introduction to statistical computing :  |b a simulation-based approach /  |c Jochen Voss. 
250 |a First edition. 
264 1 |a Chichester, West Sussex, UK :  |b John Wiley & Sons, Inc.,  |c 2013. 
300 |a 1 online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Wiley series in computational statistics 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record and CIP data provided by publisher; resource not viewed. 
520 |a "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"--  |c Provided by publisher. 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
650 0 |a Mathematical statistics  |x Data processing. 
650 7 |a MATHEMATICS  |x Probability & Statistics  |x General.  |2 bisacsh 
650 7 |a Mathematical statistics  |x Data processing.  |2 fast  |0 (OCoLC)fst01012133 
655 4 |a Electronic books. 
776 0 8 |i Print version:  |a Voss, Jochen.  |t Introduction to statistical computing.  |d Chichester, West Sussex : Wiley, 2014  |z 9781118357729  |w (DLC) 2013019321  |w (OCoLC)841894071 
830 0 |a Wiley series in computational statistics. 
856 4 0 |u https://doi.org/10.1002/9781118728048  |z Full Text via HEAL-Link 
994 |a 92  |b DG1 

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