Robust mixture modelling using the t distribution
Statistics and Computing
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Editorial: Special issue on adaptive Monte Carlo methods
Statistics and Computing
Adaptive methods for sequential importance sampling with application to state space models
Statistics and Computing
Flow-based Bayesian estimation of nonlinear differential equations for modeling biological networks
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Sequential Monte Carlo on large binary sampling spaces
Statistics and Computing
Proceedings of the Winter Simulation Conference
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In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the performance of importance sampling, as measured by an entropy criterion. The method, called M-PMC, is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performance of the proposed scheme is studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme.