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Open AccessJournal ArticleDOI

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Nikolas Kantas, +3 more
- 01 Jan 2009 - 
- Vol. 42, Iss: 10, pp 774-785
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TLDR
The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models and discuss the advantages and limitations of these methods.
About: 
This article is published in IFAC Proceedings Volumes.The article was published on 2009-01-01 and is currently open access. It has received 284 citations till now. The article focuses on the topics: Particle filter & Markov chain Monte Carlo.

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Book

Bayesian Filtering and Smoothing

Simo Srkk
TL;DR: This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework, learning what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages.
Journal ArticleDOI

Particle approximations of the score and observed information matrix in state space models with application to parameter estimation

G. Poyiadjis, +2 more
- 01 Mar 2011 - 
TL;DR: This work presents two particle algorithms to compute the score vector and observed information matrix recursively in nonlinear non-Gaussian state space models and shows how both methods can be used to perform batch and recursive parameter estimation.
Posted Content

SMC^2: an efficient algorithm for sequential analysis of state-space models

Nicolas Chopin, +2 more
- 07 Jan 2011 - 
TL;DR: The SMC2 algorithm is proposed, a sequential Monte Carlo algorithm, defined in the θ‐dimension, which propagates and resamples many particle filters in the x‐ dimension, which explores the applicability of the algorithm in both sequential and non‐sequential applications and considers various degrees of freedom.
Journal ArticleDOI

Evolution of ensemble data assimilation for uncertainty quantification using the particle filter-Markov chain Monte Carlo method

Hamid Moradkhani, +2 more
- 01 Dec 2012 - 
TL;DR: In this article, an improved particle filter with variable variance multipliers and Markov Chain Monte Carlo (MCMC) methods is proposed to improve the reliability of the particle filter for hydrologic models.
Journal ArticleDOI

Review: Fight sample degeneracy and impoverishment in particle filters: A review of intelligent approaches

Tiancheng Li, +3 more
- 01 Jun 2014 - 
TL;DR: This work is investigating methods that are particularly efficient at Particle Distribution Optimization (PDO) to fight sample degeneracy and impoverishment, with an emphasis on intelligence choices.
References
More filters
Journal ArticleDOI

Maximum likelihood from incomplete data via the EM algorithm

Arthur P. Dempster, +2 more
- 01 Sep 1977 - 
BookDOI

Sequential Monte Carlo methods in practice

Arnaud Doucet, +3 more
TL;DR: This book presents the first comprehensive treatment of Monte Carlo techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modeling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection.
Journal ArticleDOI

On sequential Monte Carlo sampling methods for Bayesian filtering

Arnaud Doucet, +2 more
- 01 Jul 2000 - 
TL;DR: An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.
Book

Monte Carlo strategies in scientific computing

Jun Liu
TL;DR: This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared.
Journal ArticleDOI

Filtering via Simulation: Auxiliary Particle Filters

Michael K. Pitt, +1 more
- 01 Jun 1999 - 
TL;DR: This article analyses the recently suggested particle approach to filtering time series and suggests that the algorithm is not robust to outliers for two reasons: the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution.
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Frequently Asked Questions (1)
Q1. What are the contributions in "An overview of sequential monte carlo methods for parameter estimation in general state-space models" ?

The aim of this paper is to present a comprehensive overview of SMC methods that have been proposed to perform static parameter estimation in general state-space models. The authors discuss the advantages and limitations of these methods.