Proceedings ArticleDOI
Fast particle smoothing: if I had a million particles
Mike Klaas,Mark Briers,Nando de Freitas,Arnaud Doucet,Simon Maskell,Dustin Lang +5 more
- pp 481-488
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TLDR
This work proposes efficient particle smoothing methods for generalized state-spaces models by integrating dual tree recursions and fast multipole techniques with forward-backward smoothers, a new generalized two-filter smoother and a maximum a posteriori (MAP) smoother.Abstract:
We propose efficient particle smoothing methods for generalized state-spaces models. Particle smoothing is an expensive O(N2) algorithm, where N is the number of particles. We overcome this problem by integrating dual tree recursions and fast multipole techniques with forward-backward smoothers, a new generalized two-filter smoother and a maximum a posteriori (MAP) smoother. Our experiments show that these improvements can substantially increase the practicality of particle smoothing.read more
Citations
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Book Chapter
A Tutorial on Particle Filtering and Smoothing: Fifteen years later
Arnaud Doucet,Adam M. Johansen +1 more
TL;DR: A complete, up-to-date survey of particle filtering methods as of 2008, including basic and advanced particle methods for filtering as well as smoothing.
Journal ArticleDOI
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo
TL;DR: This paper is intended to serve both as an introduction to SMC algorithms for nonspecialists and as a reference to recent contributions in domains where the techniques are still under significant development, including smoothing, estimation of fixed parameters and use of SMC methods beyond the standard filtering contexts.
Journal ArticleDOI
On Particle Methods for Parameter Estimation in State-Space Models
TL;DR: A comprehensive review of particle methods that have been proposed to perform static parameter estimation in state-space models is presented in this article, where the advantages and limitations of these methods are discussed.
Journal ArticleDOI
Smoothing algorithms for state–space models
TL;DR: A generalised two-filter smoothing formula is proposed which only requires approximating probability distributions and applies to any state–space model, removing the need to make restrictive assumptions used in previous approaches to this problem.
Journal ArticleDOI
An overview of sequential Monte Carlo methods for parameter estimation in general state-space models
TL;DR: 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.
References
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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A fast algorithm for particle simulations
TL;DR: An algorithm is presented for the rapid evaluation of the potential and force fields in systems involving large numbers of particles whose interactions are Coulombic or gravitational in nature, making it considerably more practical for large-scale problems encountered in plasma physics, fluid dynamics, molecular dynamics, and celestial mechanics.
Journal ArticleDOI
Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models
TL;DR: A new algorithm based on a Monte Carlo method that can be applied to a broad class of nonlinear non-Gaussian higher dimensional state space models on the provision that the dimensions of the system noise and the observation noise are relatively low.
Journal ArticleDOI
Stochastic volatility : likelihood inference and comparison with arch models
TL;DR: In this paper, Markov chain Monte Carlo sampling methods are exploited to provide a unified, practical likelihood-based framework for the analysis of stochastic volatility models, and a highly effective method is developed that samples all the unobserved volatilities at once using an approximate offset mixture model, followed by an importance reweighting procedure.