LEADER 04825nam 22007575 450 001 9910483162803321 005 20250610110516.0 010 $a9783030478452 010 $a3030478459 024 7 $a10.1007/978-3-030-47845-2 035 $a(CKB)4100000011479420 035 $a(DE-He213)978-3-030-47845-2 035 $a(MiAaPQ)EBC6362677 035 $a(MiAaPQ)EBC6523224 035 $a(Au-PeEL)EBL6362677 035 $a(OCoLC)1202759568 035 $a(PPN)255829213 035 $a(MiAaPQ)EBC29095377 035 $a(EXLCZ)994100000011479420 100 $a20201001d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 13$aAn Introduction to Sequential Monte Carlo /$fby Nicolas Chopin, Omiros Papaspiliopoulos 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XXIV, 378 p. 60 illus.) 225 1 $aSpringer Series in Statistics,$x2197-568X 311 08$a9783030478445 311 08$a3030478440 327 $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. 330 $aThis book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics. The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book. Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed. The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a ?Python corner,? which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments. 410 0$aSpringer Series in Statistics,$x2197-568X 606 $aStatistics 606 $aBig data 606 $aSystem theory 606 $aMathematical statistics$xData processing 606 $aStatistics 606 $aStatistical Theory and Methods 606 $aBig Data 606 $aComplex Systems 606 $aStatistics and Computing 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 615 0$aStatistics. 615 0$aBig data. 615 0$aSystem theory. 615 0$aMathematical statistics$xData processing. 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 615 24$aBig Data. 615 24$aComplex Systems. 615 24$aStatistics and Computing. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a519.282 676 $a518.282 700 $aChopin$b Nicolas$0845496 702 $aPapaspiliopoulos$b Omiros 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483162803321 996 $aAn introduction to Sequential Monte Carlo$91887568 997 $aUNINA