Multilevel Monte Carlo estimation of log marginal likelihood
Takashi Goda, Kei Ishikawa
TL;DR
This paper introduces an unbiased multilevel Monte Carlo estimator for the log marginal likelihood, enhancing variational Bayes methods with a novel estimation technique.
Contribution
It presents a new unbiased multilevel Monte Carlo estimator specifically designed for the log marginal likelihood in Bayesian inference.
Findings
Provides an unbiased estimator for log marginal likelihood
Demonstrates application to variational Bayes
Improves estimation accuracy in Bayesian models
Abstract
In this short note we provide an unbiased multilevel Monte Carlo estimator of the log marginal likelihood and discuss its application to variational Bayes.
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Taxonomy
TopicsStatistical Methods and Inference · Mathematical Approximation and Integration · Probabilistic and Robust Engineering Design