On stratified path sampling of the Thermodynamic Integral: computing Bayes factors for nonlinear dynamical systems models
published: Aug. 5, 2008, recorded: May 2008, views: 5788
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Bayes factors provide a means of objectively ranking a number of plausible statistical models based on their evidential support. Computing Bayes factors is far from straightforward and methodology based on thermodynamic integration can provide stable estimates of the integrated likelihood. This talk will consider a stratified sampling strategy in estimating the thermodynamic integral and will consider issues such as optimal paths and the variance of the overall estimator. The main application considered will be the computation of Bayes factors for dynamical biochemical pathway models based on systems of nonlinear ordinary differential equations (ODE). A large scale study of the ExtraCellular Regulated Kinase (ERK) pathway will be discussed where recent Small Interfering RNA (siRNA) experimental validation of the predictions made using the computed Bayes factors is presented.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !