Sequential Monte Carlo Methods in Practice |
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| Editor:
| Doucet, Arnaud
Gordon, Neil
Freitas, Nando de |
| Foreword by:
| Smith, A. |
| Series title: | Information Science and Statistics Ser. |
| ISBN: | 978-0-387-95146-1 |
| Publication Date: | Jun 2001 |
| Publisher: | Springer New York
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| Imprint: | Springer |
| Book Format: | Hardback |
| List Price: | USD $279.99 |
Book Description:
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The advent of cheap and massive computational power in conjunction with recent developments in applied statistics have stimulated many advancements in the field of sequential Monte Carlo simulation. Monte Carlo methods are very flexible in that they do not require any assumptions about the probability distributions of the data. Moreover, experimental evidence suggests that these methods lead to improved results. From a Bayesian perspective, Sequential Monte Carlo methods allow the...
More DescriptionThe advent of cheap and massive computational power in conjunction with recent developments in applied statistics have stimulated many advancements in the field of sequential Monte Carlo simulation. Monte Carlo methods are very flexible in that they do not require any assumptions about the probability distributions of the data. Moreover, experimental evidence suggests that these methods lead to improved results. From a Bayesian perspective, Sequential Monte Carlo methods allow the computation of the posterior probability distributions of interest on-line. Yet, the methods can also be applied within a maximum likelihood context. As a result, they are being applied to a large number of interesting real problems such as computer vision, econometrics, and medical prognosis.