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008 130620s2013 enk ob 001 0 eng
010 _a 2013025344
040 _aDLC
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019 _a856626109
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020 _a1118728033
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020 _a9781118728055
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020 _a111872805X
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020 _a9781118728048
_q(electronic bk.)
020 _a1118728041
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020 _a1118357728
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020 _a9781118357729
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020 _a9781299805323
020 _z9781118357729
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035 _a(OCoLC)849801365
_z(OCoLC)856626109
_z(OCoLC)859161262
_z(OCoLC)862997289
_z(OCoLC)865020993
037 _a511783
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082 0 0 _a519.501/13
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084 _aMAT029000
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049 _aMAIN
100 1 _aVoss, Jochen.
245 1 3 _aAn introduction to statistical computing :
_ba simulation-based approach /
_cJochen Voss.
250 _aFirst edition.
264 1 _aChichester, West Sussex, UK :
_bJohn Wiley & Sons, Inc.,
_c2013.
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aWiley series in computational statistics
504 _aIncludes bibliographical references and index.
588 0 _aPrint 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"--
_cProvided by publisher.
505 0 _aAn 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 _a1.7 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 _a3.4 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 _a4.4.1 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 _a6 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 _aMathematical statistics
_xData processing.
650 7 _aMATHEMATICS
_xProbability & Statistics
_xGeneral.
_2bisacsh
650 7 _aMathematical statistics
_xData processing.
_2fast
_0(OCoLC)fst01012133
655 4 _aElectronic books.
776 0 8 _iPrint version:
_aVoss, Jochen.
_tIntroduction to statistical computing.
_dChichester, West Sussex : Wiley, 2014
_z9781118357729
_w(DLC) 2013019321
_w(OCoLC)841894071
830 0 _aWiley series in computational statistics.
856 4 0 _uhttp://dx.doi.org/10.1002/9781118728048
_zWiley Online Library
994 _a92
_bDG1
999 _c20642
_d20601