Showing papers by "Serena Ng published in 2016"
TL;DR: In this article, an optimization-based reverse sampler is proposed to approximate the desired posterior distribution by first solving a sequence of simulated minimum distance problems and then reweighting by an importance ratio that depends on the prior and the volume of the Jacobian matrix.
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.
9 citations
Posted Content•
01 Jan 2016
TL;DR: In this article, an optimization-based reverse sampler is proposed to approximate the desired posterior distribution by first solving a sequence of simulated minimum distance problems and then reweighting by an importance ratio that depends on the prior and the volume of the Jacobian matrix.
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.
2 citations