article · ArXiv.org · 2025

Adaptive Pseudo-Marginal Algorithm

Abaoubida, Sarra, Bédard, Mylène, Maire, Florian

Cited by 0Open access

Abstract

The Pseudo-Marginal (PM) algorithm is a popular Markov chain Monte Carlo (MCMC) method used to sample from a target distribution when its density is inaccessible, but can be estimated with a non-negative unbiased estimator. Its performance depends on a key parameter, N, the number of iterations (or particles) used to approximate the target density. Larger values of N yield more accurate estimates but at increased running time. Previous studies has provided guidelines for selecting an optimal value of N to balance this tradeoff. However, this approach involves multiple steps and manual adjustments. To overcome these limitations, we introduce an adaptive version of the PM algorithm, where N is automatically adjusted during the iterative process toward its optimal value, thus eliminating the need for manual intervention. This algorithm ensures convergence under certain conditions. On two examples, including a real data problem on pulmonary infection in preschool children, the proposed algorithm compares favorably to the existing approach.

Cite this paper

APA 7

Abaoubida, S., Bédard, M., & Maire, F. (2025). Adaptive Pseudo-Marginal Algorithm. ArXiv.org.

MLA 9

Abaoubida, Sarra, et al. “Adaptive Pseudo-Marginal Algorithm.” ArXiv.org, 2025.

Chicago

Abaoubida, Sarra, Mylène Bédard, and Florian Maire. 2025. “Adaptive Pseudo-Marginal Algorithm.” ArXiv.org.

Harvard

Abaoubida, S., Bédard, M. and Maire, F. (2025) ‘Adaptive Pseudo-Marginal Algorithm’, ArXiv.org.

BibTeX

@article{abaoubida2025adaptive,
  title = {{Adaptive Pseudo-Marginal Algorithm}},
  author = {Abaoubida, Sarra and Bédard, Mylène and Maire, Florian},
  journal = {ArXiv.org},
  year = {2025}
}

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