Titlebook: An Introduction to Sequential Monte Carlo; Nicolas Chopin,Omiros Papaspiliopoulos Textbook 2020 Springer Nature Switzerland AG 2020 Partic

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Textbook 2020pplication 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

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Finite State-Spaces and Hidden Markov Models,d backward recursions become sums over . terms, which can be computed exactly at a cost that is shown to be .. State-space models with a finite state-space model are usually called hidden Markov models. Applying the generic algorithm to their “bootstrap” Feynman-Kac formalisation yields an exact algorithm known as the forward-backward algorithm.

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Sequential Quasi-Monte Carlo,onte Carlo where random points are replaced with low-discrepancy sequences. The advantage is that QMC estimates usually converge faster than their Monte Carlo counterparts..This chapter explains how to derive QMC particle algorithms, also called SQMC (sequential quasi-Monte Carlo) algorithms.

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https://doi.org/10.1007/978-3-030-47845-2Particle filter; Sequential Monte Carlo; Bayesian inference; Sequential learning; State-space models; Hid

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Nicolas Chopin,Omiros PapaspiliopoulosOffers a general and gentle introduction to all aspects of particle filtering: the algorithms, their uses in different areas, their computer implementation in Python and the supporting theory.Covers b

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