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Bayesian time series models /
"'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting t...
| Clasificación: | Libro Electrónico |
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| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cambridge, UK ; New York :
Cambridge University Press,
2011.
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| Colección: | Cambridge books online.
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| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | "'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice"-- "Time series appear in a variety of disciplines, from finance to physics, computer science to biology. The origins of the subject and diverse applications in the engineering and physics literature at times obscure the commonalities in the underlying models and techniques. A central aim of this book is an attempt to make modern time series techniques accessible to a broad range of researchers, based on the unifying concept of probabilistic models. These techniques facilitate access to the modern time series literature, including financial time series prediction, video-tracking, music analysis, control and genetic sequence analysis. A particular feature of the book is that it brings together leading researchers that span the more traditional disciplines of statistics, control theory, engineering and signal processing, to the more recent area machine learning and pattern recognition"-- |
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| Descripción Física: | 1 online resource (xiii, 417 pages) |
| Bibliografía: | Includes bibliographical references and index. |
| ISBN: | 9780511984679 0511984677 1139091018 9781139091015 9781139092920 1139092928 9781139091909 1139091905 1280775939 9781280775932 1107214769 9781107214767 1139092413 9781139092418 9786613686329 6613686328 |