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Bayesian Analysis of Stochastic Process Models.
This book 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. In offers an introduction...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
John Wiley & Sons,
2012.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Insua, David. | |
| 245 | 1 | 0 | |a Bayesian Analysis of Stochastic Process Models. |
| 260 | |a Hoboken : |b John Wiley & Sons, |c 2012. | ||
| 300 | |a 1 online resource (316 pages) | ||
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| 505 | 0 | |a Bayesian Analysis of Stochastic Process Models; Contents; Preface; PART ONE BASIC CONCEPTS AND TOOLS; 1 Stochastic processes; 1.1 Introduction; 1.2 Key concepts in stochastic processes; 1.3 Main classes of stochastic processes; 1.3.1 Markovian processes; 1.3.2 Poisson process; 1.3.3 Gaussian processes; 1.3.4 Brownian motion; 1.3.5 Diffusion processes; 1.4 Inference, prediction, and decision-making; 1.5 Discussion; References; 2 Bayesian analysis; 2.1 Introduction; 2.2 Bayesian statistics; 2.2.1 Parameter estimation; 2.2.2 Hypothesis testing; 2.2.3 Prediction. | |
| 505 | 8 | |a 2.2.4 Sensitivity analysis and objective Bayesian methods2.3 Bayesian decision analysis; 2.4 Bayesian computation; 2.4.1 Computational Bayesian statistics; 2.4.2 Computational Bayesian decision analysis; 2.5 Discussion; References; PART TWO MODELS; 3 Discrete time Markov chains and extensions; 3.1 Introduction; 3.2 Important Markov chain models; 3.2.1 Reversible chains; 3.2.2 Higher order chains and mixtures; 3.2.3 Discrete time Markov processes with continuous state space; 3.2.4 Branching processes; 3.2.5 Hidden Markov models; 3.3 Inference for first-order, time homogeneous, Markov chains. | |
| 505 | 8 | |a 3.3.1 Advantages of the Bayesian approach3.3.2 Conjugate prior distribution and modifications; 3.3.3 Forecasting short-term behavior; 3.3.4 Forecasting stationary behavior; 3.3.5 Model comparison; 3.3.6 Unknown initial state; 3.3.7 Partially observed data; 3.4 Special topics; 3.4.1 Reversible Markov chains; 3.4.2 Higher order chains and mixtures of Markov chains; 3.4.3 AR processes and other continuous state space processes; 3.4.4 Branching processes; 3.4.5 Hidden Markov models; 3.4.6 Markov chains with covariate information and nonhomogeneous Markov chains. | |
| 505 | 8 | |a 3.5 Case study: Wind directions at Gijón3.5.1 Modeling the time series of wind directions; 3.5.2 Results; 3.6 Markov decision processes; 3.7 Discussion; References; 4 Continuous time Markov chains and extensions; 4.1 Introduction; 4.2 Basic setup and results; 4.3 Inference and prediction for CTMCs; 4.3.1 Inference for the chain parameters; 4.3.2 Forecasting short-term behavior; 4.3.3 Forecasting long-term behavior; 4.3.4 Predicting times between transitions; 4.4 Case study: Hardware availability through CTMCs; 4.5 Semi-Markovian processes. | |
| 505 | 8 | |a 4.6 Decision-making with semi-Markovian decision processes4.7 Discussion; References; 5 Poisson processes and extensions; 5.1 Introduction; 5.2 Basics on Poisson processes; 5.2.1 Definitions and basic results; 5.2.2 Arrival and interarrival times; 5.2.3 Some relevant results; 5.3 Homogeneous Poisson processes; 5.3.1 Inference on homogeneous Poisson processes; 5.4 Nonhomogeneous Poisson processes; 5.4.1 Intensity functions; 5.4.2 Inference for nonhomogeneous Poisson processes; 5.4.3 Change points in NHPPs; 5.5 Compound Poisson processes; 5.6 Further extensions of Poisson processes. | |
| 500 | |a 5.6.1 Modulated Poisson process. | ||
| 520 | |a This book 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. In offers an introduction of MCMC and other statistical computing machinery that have pushed forward advances in Bayesian methodology. Addressing the growing interest for Bayesian analysis of more complex models, based on stochastic processes, this book aims to unite scattered information into one comprehensive and reliable. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Bayesian statistical decision theory. | |
| 650 | 0 | |a Stochastic processes. | |
| 650 | 6 | |a Théorie de la décision bayésienne. | |
| 650 | 6 | |a Processus stochastiques. | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x Bayesian Analysis. |2 bisacsh | |
| 650 | 7 | |a Bayesian statistical decision theory |2 fast | |
| 650 | 7 | |a Stochastic processes |2 fast | |
| 700 | 1 | |a Ruggeri, Fabrizio. | |
| 700 | 1 | |a Wiper, Mike. | |
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| 776 | 0 | 8 | |i Print version: |a Insua, David. |t Bayesian Analysis of Stochastic Process Models. |d Hoboken : John Wiley & Sons, ©2012 |z 9780470744536 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=877779 |z Texto completo |
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