JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 278
Type: Topic Contributed
Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #311166 View Presentation
Title: On the Prior and Posterior Distributions Used in Graphical Modeling
Author(s): Marco Scutari*+
Companies: University College London
Keywords: Markov networks ; Bayesian networks ; random graphs ; structure learning ; multivariate discrete distributions
Abstract:

Graphical model learning and inference are often performed using Bayesian techniques. In particular, learning is usually performed in two separate steps. First, the graph structure is learned from the data; then the parameters of the model are estimated conditional on that graph structure. While the probability distributions involved in this second step have been studied in depth, those used in the first step have not been explored in as much detail. If we look at them as a function of the possible edges of the graph, their properties define measures of structural variability for both Bayesian and Markov networks. Such measures have the advantage of an intuitive geometric interpretation, and can be used to improve the estimation of optimal values for tuning parameters and for model validation. Furthermore, they provide useful guidelines in defining new priors with desirable properties.


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

Back to the full JSM 2014 program




2014 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Professional Development program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.