Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models

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http://hdl.handle.net/10138/174551

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Gutmann , M U & Corander , J 2016 , ' Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models ' , Journal of Machine Learning Research , vol. 17 .

Title: Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
Author: Gutmann, Michael U.; Corander, Jukka
Contributor: University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Department of Mathematics and Statistics
Date: 2016-08
Language: eng
Number of pages: 47
Belongs to series: Journal of Machine Learning Research
ISSN: 1532-4435
URI: http://hdl.handle.net/10138/174551
Abstract: Our paper deals with inferring simulator-based statistical models given some observed data. A simulator-based model is a parametrized mechanism which specifies how data are generated. It is thus also referred to as generative model. We assume that only a finite number of parameters are of interest and allow the generative process to be very general; it may be a noisy nonlinear dynamical system with an unrestricted number of hidden variables. This weak assumption is useful for devising realistic models but it renders statistical inference very difficult. The main challenge is the intractability of the likelihood function. Several likelihood-free inference methods have been proposed which share the basic idea of identifying the parameters by finding values for which the discrepancy between simulated and observed data is small. A major obstacle to using these methods is their computational cost. The cost is largely due to the need to repeatedly simulate data sets and the lack of knowledge about how the parameters affect the discrepancy. We propose a strategy which combines probabilistic modeling of the discrepancy with optimization to facilitate likelihood-free inference. The strategy is implemented using Bayesian optimization and is shown to accelerate the inference through a reduction in the number of required simulations by several orders of magnitude.
Subject: intractable likelihood
latent variables
Bayesian inference
approximate Bayesian computation
computational efficiency
113 Computer and information sciences
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