SMS scnews item created by John Ormerod at Mon 31 Aug 2015 1022
Type: Seminar
Distribution: World
Expiry: 4 Sep 2015
Calendar1: 4 Sep 2015 1400-1500
CalLoc1: Carslaw 173
Auth: jormerod@pjormerod5.pc (assumed)

# Statistics Seminar: Min Ngoc Tran (USyd) -- Exact Bayesian Inference for Approximate Bayesian Computation

Abstract: Approximate Bayesian Computation (ABC) is a powerful method for carrying
out Bayesian inference when the likelihood is computationally intractable. However,
a drawback of ABC is that it is an approximate method that suffers from a systematic
error because it is necessary to set a tolerance level to make the computation
tractable. The issue of how to optimally set this tolerance level has been the
subject of extensive research. We propose an ABC algorithm based on importance
sampling that estimates expectations with respect to the exact posterior distribution
given the observed summary statistics. This overcomes the need to select the tolerance
level. By exact we mean that there is no systematic error and the Monte Carlo can be
made arbitrarily small by increasing the number of importance samples. We provide a
formal justification for of the method and study its convergence properties. The
method is illustrated in two applications and the empirical results suggest that the
proposed ABC based estimators consistently converge to the true values as the number
of importance samples increases.

This is joint work with Robert Kohn (UNSW).


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