An Introduction to Sequential Monte Carlo / by Nicolas Chopin, Omiros Papaspiliopoulos.

Format:

Book

Author/Creator:

Chopin, Nicolas, author.

Papaspiliopoulos, Omiros, author.

Contributor:

SpringerLink (Online service)

Series:

Mathematics and Statistics (SpringerNature-11649)

Springer series in statistics 0172-7397

Springer Series in Statistics, 0172-7397

Language:

English

Subjects (All):

Statistics.

Big data.

Sociophysics.

Econophysics.

Statistical Theory and Methods.

Big Data.

Data-driven Science, Modeling and Theory Building.

Statistics and Computing/Statistics Programs.

Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Local Subjects:

Statistical Theory and Methods.

Big Data.

Data-driven Science, Modeling and Theory Building.

Statistics and Computing/Statistics Programs.

Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

Physical Description:

1 online resource (XXIV, 378 pages) : 60 illustrations.

Edition:

First edition 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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.

Contents:

1 Preface

2 Introduction to state-space models

3 Beyond state-space models

4 Introduction to Markov processes

5 Feynman-Kac models: definition, properties and recursions

6 Finite state-spaces and hidden Markov models

7 Linear-Gaussian state-space models

8 Importance sampling

9 Importance resampling

10 Particle filtering

11 Convergence and stability of particle filters

12 Particle smoothing

13 Sequential quasi-Monte Carlo

14 Maximum likelihood estimation of state-space models

15 Markov chain Monte Carlo

16 Bayesian estimation of state-space models and particle MCMC

17 SMC samplers

18 SMC2, sequential inference in state-space models

19 Advanced topics and open problems.

Other Format:

Printed edition:

ISBN:

978-3-030-47845-2

9783030478452

Access Restriction:

Restricted for use by site license.