rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: An
    title: Introduction to Sequential Monte Carlo
  name:
    name part: Chopin, Nicolas.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Papaspiliopoulos, Omiros.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2020
    edition: 1st ed. 2020.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XXIV, 378 pages 60 illustrations)
  abstract: 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.
  table of 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.
  note: by Nicolas Chopin, Omiros Papaspiliopoulos.
  subject:
    topic: Statistics
  subject:
    topic: Big data
  subject:
    topic: Sociophysics
  subject:
    topic: Econophysics
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Big Data
  subject:
    topic: Data-driven Science, Modeling and Theory Building
  subject:
    topic: Statistics and Computing/Statistics Programs
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Springer Series in Statistics
  identifier: 9783030478452
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record creation date: 201001
    record change date: 20230330185006.4
    record identifier: in00004427541