Chopin N An Introduction to Sequential Monte Carlo 2020 torrent

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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 may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a “Python corner,” which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.
Preface
Introduction to State-Space Models
Beyond State-Space Models
Introduction to Markov Processes
Feynman-Kac Models: Definition, Properties and Recursions
Finite State-Spaces and Hidden Markov Models
Linear-Gaussian State-Space Models
Importance Sampling
Importance Resampling
Particle Filtering
Convergence and Stability of Particle Filters
Particle Smoothing
Sequential Quasi-Monte Carlo
Maximum Likelihood Estimation of State-Space Models
Markov Chain Monte Carlo
Bayesian Estimation of State-Space Models and Particle MCMC
SMC Samplers
SMC2, Sequential Inference in State-Space Models
Advanced Topics and Open Problems