Adaptive sequential Monte Carlo by means of mixture of experts

By Julien Cornebise, Eric Moulines, Jimmy Olsson. 2011

11 citations recorded by the source corpus.

Appropriately designing the proposal kernel of particle filters is an issue of significant importance, since a bad choice may lead to deterioration of the particle sample and, consequently, waste of computational power. In this paper we introduce a novel algorithm adaptively approximating the so-called optimal proposal kernel by a mixture of integrated curved exponential distributions with logistic weights. This family of distributions, referred to as mixtures of experts, is broad enough to be used in the presence of multi-modality or strongly skewed distributions. The mixtures are fitted, via online-EM methods, to the optimal kernel through minimisation of the Kullback-Leibler divergence between the auxiliary target and instrumental distributions of the particle filter. At each iteration of the particle filter, the algorithm is required to solve only a single optimisation problem for the whole particle sample, yielding an algorithm with only linear complexity. In addition, we illustrate in a simulation study how the method can be successfully applied to optimal filtering in nonlinear state-space models.

Subfields: Artificial Intelligence, Probabilistic & Bayesian Methods

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Citations in the corpus

Inference Networks for Sequential Monte Carlo in Graphical Models (2016, methodology): “…algorithms requires either active (human) work to design an appropriate proposal distribution prior to sampling, or using an online estimation procedure to approximate the optimal proposal during inference (as in e.g. Van Der Merwe et al. (2000) or Cornebise et al. (2014) for state-space models).”