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Activity Number: 87 - Invited ePoster Session: a Statistical Smörgåsbord
Type: Invited
Date/Time: Sunday, July 29, 2018 : 8:30 PM to 10:30 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #328384
Title: Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations
Author(s): Taylor Gene Pospisil*
Companies: Carnegie Mellon University
Keywords: Approximate Bayesian Computation; Conditional Density Estimation

Approximate Bayesian Computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings using a forward model. Despite the recent interest in ABC, high-dimensional data and costly simulations still remain a bottle-neck. There is also no consensus as to how to best assess the performance of such methods. We show how a nonparametric conditional density estimation (CDE) framework can help address three key challenges in ABC, namely: (i) how to efficiently estimate the posterior distribution with limited simulations and different types of data, (ii) how to compare the performance of ABC and related methods with CDE as a goal, and (iii) how to efficiently choose among a very large set of summary statistic based on a CDE loss. We provide both theoretical and empirical evidence to justify the use of such procedures and describe settings where standard ABC may fail.

Authors who are presenting talks have a * after their name.

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