| 期刊全称 | An Introduction to Sequential Monte Carlo | | 影响因子2023 | Nicolas Chopin,Omiros Papaspiliopoulos | | 视频video | | | 发行地址 | Offers a general and gentle introduction to all aspects of particle filtering: the algorithms, their uses in different areas, their computer implementation in Python and the supporting theory.Covers b | | 学科分类 | Springer Series in Statistics | | 图书封面 |  | | 影响因子 | .This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics..The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book..Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed..The book | | Pindex | Textbook 2020 |
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发表于 2025-3-21 19:29:20
