Work

Marginalization and conditioning for LWF chain graphs

Year: 2016

Type: article

Abstract: In this paper, we deal with the problem of marginalization over and conditioning on two disjoint subsets of the node set of chain graphs (CGs) with the LWF Markov property. For this purpose, we define the class of chain mixed graphs (CMGs) with three types of edges and, for this class, provide a separation criterion under which the class of CMGs is stable under marginalization and conditioning and contains the class of LWF CGs as its subclass. We provide a method for generating such graphs after marginalization and conditioning for a given CMG or a given LWF CG. We then define and study the class of anterial graphs, which is also stable under marginalization and conditioning and contains LWF CGs, but has a simpler structure than CMGs.

Source: The Annals of Statistics

DOI: 10.1214/16-aos1451

Author: Kayvan Sadeghi

Institution: University of Cambridge

Language: English

Topic: Bayesian Modeling and Causal Inference

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Defense Advanced Research Projects Agency, U.S. Air Force, Advanced Research Projects Agency, Air Force Office of Scientific Research

Awards: G7090116110, G7391891542