Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions

Wang, Hongqiao and Li, Jinglai (2018) Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions NEURAL COMPUTATION, 30 (11). pp. 3072-3094. ISSN 0899-7667, 1530-888X

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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:	16 Jun 2026 13:10
DOI:	10.1162/neco_a_01127
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3029235
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• Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions (deposited 28 Sep 2018 07:19)
  • Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions (deposited 30 Nov 2018 09:40) [Currently Displayed]