Maximum likelihood fitting of acyclic directed mixed graphs to binary data

Abstract

Acyclic directed mixed graphs, also known as semi-Markov models represent the conditional independence structure induced on an observed margin by a DAG model with latent variables. In this paper we present the first method for fitting these models to binary data using maximum likelihood estimation.

Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010)

References in corpus (3)

• Likelihood ratio tests and singularities
• Pearl's Calculus of Intervention Is Complete
• Cumulative distribution networks and the derivative-sum-product algorithm

Cited by in corpus (6)

• Graphs for margins of Bayesian networks
• Nested Markov Properties for Acyclic Directed Mixed Graphs
• Markovian acyclic directed mixed graphs for discrete data
• Margins of discrete Bayesian networks
• Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
• Smooth, identifiable supermodels of discrete DAG models with latent variables