A Likelihood-Free Reverse Sampler of the Posterior Distribution

Jean-Jacques Forneron and Serena Ng ()

A chapter in Essays in Honor of Aman Ullah, 2016, vol. 36, pp 389-415 from Emerald Group Publishing Limited

Abstract: This paper considers properties of an optimization-based sampler for targeting the posterior distribution when the likelihood is intractable. It uses auxiliary statistics to summarize information in the data and does not directly evaluate the likelihood associated with the specified parametric model. Our reverse sampler approximates the desired posterior distribution by first solving a sequence of simulated minimum distance problems. The solutions are then reweighted by an importance ratio that depends on the prior and the volume of the Jacobian matrix. By a change of variable argument, the output consists of draws from the desired posterior distribution. Optimization always results in acceptable draws. Hence, when the minimum distance problem is not too difficult to solve, combining importance sampling with optimization can be much faster than the method of Approximate Bayesian Computation that by-passes optimization.

Keywords: Approximate Bayesian Computation; indirect inference; importance sampling; C22; C23 (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-905320160000036020

DOI: 10.1108/S0731-905320160000036020

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