| 020 | $a9783030478452 |
| 082 | $a519.5 |
| 099 | $aOnline resource: Springer |
| 100 | $aChopin, Nicolas.$eauthor. |
| 245 | $aAn Introduction to Sequential Monte Carlo$h[EBook]$cby Nicolas Chopin, Omiros Papaspiliopoulos. |
| 250 | $a1st ed. 2020. |
| 260 | $aCham :$bSpringer International Publishing :$bImprint: Springer,$c2020. |
| 300 | $aXXIV, 378 p. 60 illus.$bonline resource. |
| 336 | $atext |
| 338 | $aonline resource |
| 440 | $aSpringer Series in Statistics,$x0172-7397 |
| 505 | $a1 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. |
| 520 | $aThis 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. |
| 533 | $aDigital book. Cham Springer Nature 2020. - Mode of access: World Wide Web. System requirements: Internet Explorer 6.0 (or higher) or Firefox 2.0 (or higher). Available as searchable text in PDF format |
| 538 | $a - Online access to this digital book is restricted to subscription institutions through IP address (only for SISSA internal users). |
| 700 | $aPapaspiliopoulos, Omiros.$eauthor. |
| 710 | $aSpringerLink (Online service) |
| 830 | $aSpringer Series in Statistics,$x0172-7397 |
| 856 | $uhttps://doi.org/10.1007/978-3-030-47845-2 |
