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An Introduction to Sequential Monte Carlo

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 fina...

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Bibliographic Details
Main Authors: Chopin, Nicolas, Papaspiliopoulos, Omiros (Author)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2020, 2020
Edition:1st ed. 2020
Series:Springer Series in Statistics
Subjects:
Data-driven Science, Modeling And Theory Building
Big Data
Sociophysics
Statistical Theory And Methods
Statistics 
Statistics And Computing/statistics Programs
Econophysics
Statistics For Engineering, Physics, Computer Science, Chemistry And Earth Sciences
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
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

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