Stochastic Gradient Population Monte Carlo

Published in IEEE Signal Processing Letters, 2019

The population Monte Carlo (PMC) algorithm is a powerful adaptive importance sampling (AIS) methodology used for estimating expected values of random quantities w.r.t. some target probability distribution. At each iteration, a Markov transition kernel is used to propagate a set of particles. Importance weights of the particles are computed and then used to resample the particles that are most representative of the target distribution. At the end of the algorithm, the set of all particles and weights can be used to perform estimation. The resampling step is an adaptive mechanism of the PMC algorithm that allows for particles to locate the most significant regions of the sampling space. In this paper, we generalize the adaptation procedure of PMC sampling by providing a perspective based on stochastic optimization rather than resampling. The proposed method is more flexible than standard PMC as it allows the parameter adaptation to be resolved using any stochastic optimization method. We show that under certain conditions, the standard PMC algorithm is a special case of the proposed approach.

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