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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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