Bayesian Analysis of Stochastic Process Models

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John Wiley & Sons, 2 abr 2012 - 316 páginas
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Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models.

Key features:

  • Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment.
  • Provides a thorough introduction for research students.
  • Computational tools to deal with complex problems are illustrated along with real life case studies
  • Looks at inference, prediction and decision making.

Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.

 

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Índice

Contents
Stochastic processes
References
References
Discrete time Markov chains and extensions
CONTENTS
Continuous time Markov chains and extensions 82
Poisson processes and extensions 105
13
22
25
34
Reliability 200
45
Discrete event simulation 226
References 240

References 131
PART THREE APPLICATIONS
7
CONTENTS
Appendix A Main distributions 273
Appendix B Generating functions and the LaplaceStieltjes transform 283
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Otras ediciones - Ver todo

Bayesian Analysis of Stochastic Process Models
David Insua,Fabrizio Ruggeri,Mike Wiper
Vista previa restringida - 2012
Bayesian Analysis of Stochastic Process Models
David Insua,Fabrizio Ruggeri,Mike Wiper
No hay ninguna vista previa disponible - 2012

Términos y frases comunes

algorithm ANALYSIS OF STOCHASTIC applied approach approximation arrival assume BAYESIAN ANALYSIS Bayesian inference Chapter computational conditional considered continuous decision defined Definition density dependence described discrete discussed Edition equilibrium estimate example expected experiment exponential EXTENSIONS failures Figure follows gamma Gaussian process given illustrated important independent inference initial intensity function interest interval introduced Journal JWST172-Ruggeri March known likelihood function Markov chain matrix mean methods mixture Monte Carlo NHPP normal Note observed obtain occur Operations optimal parameters particular performed period Poisson process possible posterior distribution predictive presented prior distribution probability problem properties queueing system random variables reliability respectively Ruggeri sample Second shows simulation space standard Statistical Stochastic Differential Equations STOCHASTIC PROCESS MODELS Suppose Table Theorem Theory transition values whereas York

Sobre el autor (2012)

Fabrizio Ruggeri, Research Director, CNR IMATI, Milano, Italy.

Michael P. Wiper, Associate Professor in Statistics, Department of Statistics, Universidad Carlos III de Madrid, Spain.

David Rios Insua, Professor of Statistics and Operations Research, Department of Statistics and Operations Research, Universidad Rey Juan Carlos, Spain.

Información bibliográfica

QR code for Bayesian Analysis of Stochastic Process Models
TítuloBayesian Analysis of Stochastic Process Models
Wiley Series in Probability and Statistics
AutoresDavid Insua, Fabrizio Ruggeri, Mike Wiper
EditorJohn Wiley & Sons, 2012
ISBN1118304039, 9781118304037
N.º de páginas316 páginas
  
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