| 书目名称 | Inference in Hidden Markov Models |
| 编辑 | Olivier Cappé,Eric Moulines,Tobias Rydén |
| 视频video | http://file.papertrans.cn/465/464593/464593.mp4 |
| 概述 | Builds on recent developments, both at the foundational level and the computational level, to present a self-contained view.Includes supplementary material: |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | .Hidden Markov models have become a widely used class of statistical models with applications in diverse areas such as communications engineering, bioinformatics, finance and many more. This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states...In a unified way the book covers both models with finite state spaces, which allow for exact algorithms for filtering, estimation etc. and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Simulation in hidden Markov models is addressed in five different chapters that cover both Markov chain Monte Carlo and sequential Monte Carlo approaches. Many examples illustrate the algorithms and theory. The book also carefully treats Gaussian linear state-space models and their extensions and it contains a chapter on general Markov chain theory and probabilistic aspects of hidden Markov models...This volume will suit anybody with an |
| 出版日期 | Book 2005 |
| 关键词 | Analysis; Estimator; Excel; Measure; Probability theory; Statistical Models; Stochastic Processes; bioinfor |
| 版次 | 1 |
| doi | https://doi.org/10.1007/0-387-28982-8 |
| isbn_softcover | 978-1-4419-2319-6 |
| isbn_ebook | 978-0-387-28982-3Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag New York 2005 |
|Archiver|手机版|小黑屋|
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