Bayesian Analysis of Stochastic Process Models

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John Wiley & Sons, 2 abr. 2012 - 320 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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