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Activity Number: 455 - Recent Advances in Bayesian Computation: Theory and Methods
Type: Topic Contributed
Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #306616 Presentation
Title: Recent Advances in Bayesian Computation: Theory and Methods
Author(s): Murali Haran* and Jaewoo Park
Companies: Penn State University and Penn State University
Keywords: doubly intractable distribution; normalizing function; importance sampling; point process; Markov chain Monte Carlo; exponential random graph model

Doubly intractable distributions arise in many settings, for example in Markov models for point processes and exponential random graph models for networks. Bayesian inference for these models is challenging because they involve intractable normalizing "constants" that are actually functions of the parameters of interest. Although several clever computational methods have been developed for these models, each method suffers from computational issues that makes it computationally burdensome or even infeasible for many problems. I will discuss a framework for understanding existing algorithms, as well as a proposal for a new algorithm that replaces Monte Carlo approximations to the normalizing function with a Gaussian process-based approximation. This is joint work with Jaewoo Park.

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

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