NDLI: Nonparametric particle filtering approaches for identification and inference in nonlinear state-space dynamic systems

Content Provider	Springer Nature Link
Author	Gauchi, Jean Pierre Vila, Jean Pierre
Copyright Year	2012
Abstract	Most system identification approaches and statistical inference methods rely on the availability of the analytic knowledge of the probability distribution function of the system output variables. In the case of dynamic systems modelled by hidden Markov chains or stochastic nonlinear state-space models, these distributions as well as that of the state variables themselves, can be unknown or untractable. In that situation, the usual particle Monte Carlo filters for system identification or likelihood-based inference and model selection methods have to rely, whenever possible, on some hazardous approximations and are often at risk. This review shows how a recent nonparametric particle filtering approach can be efficiently used in that context, not only for consistent filtering of these systems but also to restore these statistical inference methods, allowing, for example, consistent particle estimation of Bayes factors or the generalisation of model parameter change detection sequential tests.Real-life applications of these particle approaches to a microbiological growth model are proposed as illustrations.
Starting Page	523
Ending Page	533
Page Count	11
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	23
Issue Number	4
e-ISSN	15731375
Language	English
Publisher	Springer US
Publisher Date	2012-03-30
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Nonlinear filtering Particle filtering Nonparametric filtering State-space model Inference tests Bayes factor CUSUM test Sensitivity indices Statistics and Computing/Statistics Programs Artificial Intelligence (incl. Robotics) Statistical Theory and Methods Probability and Statistics in Computer Science
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

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