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Activity Number: 693
Type: Contributed
Date/Time: Thursday, August 4, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #320626 View Presentation
Title: Toward Efficient MCMC Algorithms for Doubly Intractable Distributions
Author(s): Jaewoo Park* and Murali Haran
Companies: Penn State University and Penn State University
Keywords: Markov Chain Monte Carlo ; Intractable likelihoods ; Auxiliary variable ; Point process ; Network models ; normalizing functions
Abstract:

Inference for models with intractable normalizing functions, such as some point processes and network models, poses serious computational challenges. We will discuss the relative merits and disadvantages of auxiliary variable Markov chain Monte Carlo (MCMC) and other approximate approaches that solve this problem. Each of these methods suffers from computational issues that makes it impractical in many settings. We propose novel algorithms that provide computational gains over existing methods, discuss some theoretical issues, and illustrate the practical application of our methods to real data examples.


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

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