Query Efficient Posterior Estimation in Scientific Experiments via Bayesian Active Learning

Understand

A common problem in disciplines of applied Statistics research such as Astrostatistics is of estimating the posterior distribution of relevant parameters.

• Typically, the likelihoods for such models are computed via expensive experiments such as cosmological simulations of the universe.
• An urgent challenge in these research domains is to develop methods that can estimate the posterior with few likelihood evaluations.
• In this paper, we study active posterior estimation in a Bayesian setting when the likelihood is expensive to evaluate.