A simple approach to maximum intractable likelihood estimation

By F. Rubio, Adam M. Johansen. 2013

53 citations recorded by the source corpus.

Approximate Bayesian Computation (ABC) can be viewed as an analytic approximation of an intractable likelihood coupled with an elementary simulation step. Such a view, combined with a suitable instrumental prior distribution permits maximum-likelihood (or maximum-a-posteriori) inference to be conducted, approximately, using essentially the same techniques. An elementary approach to this problem which simply obtains a nonparametric approximation of the likelihood surface which is then maximised is developed here and the convergence of this class of algorithms is characterised theoretically. The use of non-sufficient summary statistics in this context is considered. Applying the proposed method to four problems demonstrates good performance. The proposed approach provides an alternative for approximating the maximum likelihood estimator (MLE) in complex scenarios.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7d2af273c557e0a0e17a77ab68b7f3db2ab39f90

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

On the Asymptotic Efficiency of Approximate Bayesian Computation Estimators (2015, methodology): “mited, and theory to date has often focussed on specic aspects of ABC. By ignoring the Monte Carlo error, the asymptotic properties of some ABC estimators of the parameter are analysed. For example, [39,26] show the consistency of the maximum a posteriori estimator of the ABC posterior density; [14]”