Variance Reduction for the Independent Metropolis Sampler

• URL: http://arxiv.org/abs/2406.17699v2
• Date: Tue, 15 Oct 2024 22:05:38 GMT
• Title: Variance Reduction for the Independent Metropolis Sampler
• Authors: Siran Liu, Petros Dellaportas, Michalis K. Titsias,
• Abstract summary: We prove that if $pi$ is close enough under KL divergence to another density $q$, an independent sampler that obtains samples from $pi$ achieves smaller variance than i.i.d. sampling from $pi$. We propose an adaptive independent Metropolis algorithm that adapts the proposal density such that its KL divergence with the target is being reduced.
• Score: 11.074080383657453
• License:
• Abstract: Assume that we would like to estimate the expected value of a function $F$ with respect to an intractable density $\pi$, which is specified up to some unknown normalising constant. We prove that if $\pi$ is close enough under KL divergence to another density $q$, an independent Metropolis sampler estimator that obtains samples from $\pi$ with proposal density $q$, enriched with a variance reduction computational strategy based on control variates, achieves smaller asymptotic variance than i.i.d.\ sampling from $\pi$. The control variates construction requires no extra computational effort but assumes that the expected value of $F$ under $q$ is analytically available. We illustrate this result by calculating the marginal likelihood in a linear regression model with prior-likelihood conflict and a non-conjugate prior. Furthermore, we propose an adaptive independent Metropolis algorithm that adapts the proposal density such that its KL divergence with the target is being reduced. We demonstrate its applicability in a Bayesian logistic and Gaussian process regression problems and we rigorously justify our asymptotic arguments under easily verifiable and essentially minimal conditions.

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