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Communication Dans Un Congrès Année : 2019

Langevin-based Strategy for Efficient Proposal Adaptation in Population Monte Carlo

Résumé

Population Monte Carlo (PMC) algorithms are a family of adaptive importance sampling (AIS) methods for approximating integrals in Bayesian inference. In this paper, we propose a novel PMC algorithm that combines recent advances in the AIS and the optimization literatures. In such a way, the proposal densities are adapted according to the past weighted samples via a local resampling that preserves the diversity, but we also exploit the geometry of the targeted distribution. A scaled Langevin strategy with Newton-based scaling metric is retained for this purpose, allowing to adapt jointly the means and the covariances of the proposals, without needing to tune any extra parameter. The performance of the proposed technique is clearly superior in two numerical examples at the cost of a reasonable computational complexity increment.
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Dates et versions

hal-02431677 , version 1 (08-01-2020)

Identifiants

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Víctor Elvira, Emilie Chouzenoux. Langevin-based Strategy for Efficient Proposal Adaptation in Population Monte Carlo. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing, May 2019, Brighton, United Kingdom. ⟨10.1109/ICASSP.2019.8682284⟩. ⟨hal-02431677⟩
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