The Library
Asymptotic model selection and identifiability of directed tree models with hidden variables
Tools
Zwiernik, Piotr (2010) Asymptotic model selection and identifiability of directed tree models with hidden variables. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2010 (No.6).

PDF
WRAP_Zwiernik_1006w.pdf  Published Version  Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader Download (599Kb) 
Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...
Abstract
The standard Bayesian Information Criterion (BIC) is derived under
some regularity conditions which are not always satisfied by the graphical
models with hidden variables. In this paper we derive the BIC score for
Bayesian networks in the case when the data is binary and the underlying
graph is a rooted tree and all the inner nodes represent hidden variables. This
provides a direct generalization of a similar formula given by Rusakov and
Geiger in [10]. Geometric results obtained in this paper are complementary to
the results in the previous paper [18] extending our understanding of this class
of models. The main tool used in this paper is the connection between asymptotic
approximation of Laplace integrals and the real logcanonical threshold.
Item Type:  Working or Discussion Paper (Working Paper) 

Subjects:  Q Science > QA Mathematics 
Divisions:  Faculty of Science > Statistics 
Library of Congress Subject Headings (LCSH):  Bayesian statistical decision theory, Trees (Graph theory) 
Series Name:  Working papers 
Publisher:  University of Warwick. Centre for Research in Statistical Methodology 
Place of Publication:  Coventry 
Official Date:  2010 
Volume:  Vol.2010 
Number:  No.6 
Number of Pages:  27 
Status:  Not Peer Reviewed 
Access rights to Published version:  Open Access 
References:  [1] V. Arnold, S. GuseinZade, and A. Varchenko, Singularities of differentiable maps, 
URI:  http://wrap.warwick.ac.uk/id/eprint/35068 
Request changes or add full text files to a record
Actions (login required)
View Item 
Downloads
Downloads per month over past year