Evidence estimation using stochastic likelihood approximations
Date
2020-04
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Stellenbosch : Stellenbosch University.
Abstract
ENGLISH ABSTRACT: We consider the problem of estimating evidence for parametric Bayesian models in the large data regime. Many existing evidence estimation algorithms scale poorly due to their need to repeatedly calculate the exact likelihood, which requires iterating over the entire data set. This inefficiency can be circumvented with the use of stochastic likelihood estimates on small sub-samples of the data set. We therefore tackle this problem by introducing stochastic gradient Monte Carlo methods for evidence estimation, our main contribution being stochastic gradient annealed importance sampling. Our approach enables efficient online evidence estimation for large data sets. SGAIS is considerably faster than previous approaches for single data sets, with improved order complexity for online estimation, without noticeable loss of accuracy.
Description
Thesis (MSc)--Stellenbosch University, 2020.
Keywords
Evidence -- Estimation, Stochastic approximations, Parameter estimation, Simulated annealing (Mathematics), Big data, UCTD