Michael Gutmann (University of Helsinki): Bayesian Optimization for Likelihood-Free Estimation

In approximate Bayesian computation, sampled parameters are retained with high probability if the sampled and observed data are “close”. Similarly, in point estimation with indirect inference, a parameter is sought which makes the sampled and observed data close. Unfortunately, the sampled and observed data are rarely close enough which makes both approaches to estimation computationally costly. In this work, we propose to use Bayesian optimization to increase the computational efficiency in indirect inference. The same principle may be used in ABC.
Joint work with Jukka Corander

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