Recursive Bayesian estimation using piece-wise constant approximations
Automatica (Journal of IFAC)
Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates
Mathematics and Computers in Simulation - IMACS sponsored Special issue on the second IMACS seminar on Monte Carlo methods
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Advanced point-mass method for nonlinear state estimation
Automatica (Journal of IFAC)
Recursive bayesian estimation using gaussian sums
Automatica (Journal of IFAC)
Information bounds and quick detection of parameter changes in stochastic systems
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
An adaptive sequential Monte Carlo method for approximate Bayesian computation
Statistics and Computing
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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.