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- Author: Insuari
- Publisher: John Wiley & Sons
- ISBN-10: 0470744537
- ISBN-13: 9780470744536
- Format: 15.5 x 23.1 x 2.3 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
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.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 20d.03:36:03
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Insuari
- Publisher: John Wiley & Sons
- ISBN-10: 0470744537
- ISBN-13: 9780470744536
- Format: 15.5 x 23.1 x 2.3 cm, hardcover
- Language: English English
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.
Reviews