Multilevel Monte Carlo estimation of log marginal likelihood

This work addresses a computational bottleneck in Bayesian inference for statisticians and machine learning practitioners, but it appears incremental as it builds on existing multilevel Monte Carlo methods.

The paper tackles the problem of estimating log marginal likelihoods, presenting an unbiased multilevel Monte Carlo estimator and applying it to variational Bayes.