Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions

Wang, Hongqiao and Li, Jinglai ORCID: 0000-0001-7980-6901 (2018) Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions. NEURAL COMPUTATION, 30 (11). pp. 3072-3094.

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Abstract

We consider Bayesian inference problems with computationally intensive likelihood functions. We propose a Gaussian process (GP)-based method to approximate the joint distribution of the unknown parameters and the data, built on recent work (Kandasamy, Schneider, & Póczos, 2015). In particular, we write the joint density approximately as a product of an approximate posterior density and an exponentiated GP surrogate. We then provide an adaptive algorithm to construct such an approximation, where an active learning method is used to choose the design points. With numerical examples, we illustrate that the proposed method has competitive performance against existing approaches for Bayesian computation.

Item Type:	Article
Uncontrolled Keywords:	stat.CO, stat.CO, stat.ML
Depositing User:	Symplectic Admin
Date Deposited:	30 Nov 2018 09:40
Last Modified:	19 Jan 2023 01:11
DOI:	10.1162/neco_a_01127
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3029235

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