Comparing Score-Based Methods for Estimating Bayesian Networks Using the Kullback–Leibler Divergence

Jessica Kasza and Patty Solomon

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 1, 135-152

Abstract: We recently proposed two methods for estimating Bayesian networks from high-dimensional non-independent and identically distributed data containing exogenous variables and random effects (Kasza et al., 2012). The first method is fully Bayesian, and the second is “residual”-based, accounting for the effects of the exogenous variables by utilizing the notion of restricted maximum likelihood. We describe the methods and compare their performance using the Kullback–Leibler divergence, which provides a natural framework for comparing posterior distributions. In applications where the exogenous variables are not of primary interest, we show that the potential loss of information about parameters of interest is typically small.

Date: 2015
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2012.735329 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:44:y:2015:i:1:p:135-152

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2012.735329

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().