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Activity Number: 505
Type: Contributed
Date/Time: Thursday, August 10, 2006 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #305440
Title: Continuous Contour Monte Carlo for Marginal Density Estimation
Author(s): Faming Liang*+
Companies: Texas A&M University
Address: Department of Statistics, College Station, TX, 77843,
Keywords: marginal density estimation ; contour Monte Carlo ; stochastic approximation ; Wang-Landau algorithm ; autologistic model ; kernel density estimation
Abstract:

In this work, we proposed the continuous contour Monte Carlo (CCMC) algorithm as a general algorithm for estimating the marginal density function. CCMC can be regarded as a continuous version of the contour Monte Carlo (CMC) algorithm, proposed in the literature recently. It abandons the concept of sample space partition, on which CMC is based, and makes use of kernel density estimation techniques to improve its efficiency. CCMC was applied to estimate the normalizing constant function for an autologistic model. The estimate was then used in place of the true normalizing constant function in a Bayesian analysis for the model. The superiority of the Bayesian approach for this model justifies the value of CCMC in Bayesian computation.

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