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Activity Number: 250
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #306016
Title: Efficient Multiple MCMC for Bayesian Parameter Estimation in Dynamic Systems
Author(s): Samuel Wong*+
Companies: Harvard University
Address: 28 Wendell St. Apt 4, Cambridge, MA, 02138, United States
Keywords: Bayesian computation ; MCMC methods ; dynamic systems ; differential equation models ; parameter estimation

We propose an efficient MCMC scheme for estimating parameters in dynamic systems governed by a set of ordinary differential equations, which are frequently used to describe behaviors in science. The data observed are usually noisy and collected at discrete time intervals as the system evolves. Bayesian and likelihood-based inference for these systems face two main computational challenges: the unusual shapes of likelihood surfaces encountered, and the time required for numerically solving the differential equations. To address these challenges, our framework introduces a latent variable to control the noise level in the model, producing multiple chains of Monte Carlo samples of parameters to allow the coarser chains to improve convergence of the finer chains. We then improve computational efficiency by only selectively evaluating the numerical solutions, and improve estimation efficiency by combining the multiple chains. Our method is illustrated by applications to biological systems.

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