9780470744536 - Bayesian Analysis of Stochastic Process Models (Wiley Series in Probability and Statistics) von Insua, David; Ruggeri, Fabrizio; Wiper, Mike (6 Ergebnisse)

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. pp. 332.

Zustand: New. Bayesian Analysis of Stochastic Process Models provides analysis of stochastic processes from a Bayesian perspective with coverage of the main classes of stochastic processing, including modeling, computational, inference, prediction, decision-making, and important applied models based on stochastic processes. Series: Wiley Series in Probability and Statistics. Num Pages: 332 pages, Illustrations. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 159 x 235 x 22. Weight in Grams: 588. . 2012. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. 1st edition. 332 pages. 9.21x6.22x0.87 inches. In Stock.

Gebunden. Zustand: New. Bayesian analysis of complex models based on stochastic processes has seen a surge in research activity in recent years. Bayesian Analysis of Stochastic Process Models provides a unified treatment of Bayesian analysis of models based on stochastic processes.

Buch. Zustand: Neu. Neuware - 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.