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T1 - Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
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UR - http://hdl.handle.net/10138/174551
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A1 - Gutmann, Michael U.; Corander, Jukka
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Y1 - 2016
LA - eng
AB - 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 ...
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KW - intractable likelihood; latent variables; Bayesian inference; approximate Bayesian computation; computational efficiency; 113 Computer and information sciences
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