Ancestral Graph Markov Models

Abstract

This paper introduces a class of graphical independence models that is closed under marginalization and conditioning but that contains all DAG independence models. This class of graphs, called maximal ancestral graphs, has two attractive features: there is at most one edge between each pair of vertices; every missing edge corresponds to an independence relation. These features lead to a simple parametrization of the corresponding set of distributions in the Gaussian case.

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Document Details

Document Type
Technical Report
Publication Date
Apr 15, 2002
Accession Number
ADA480171

Entities

People

  • Peter Spirtes
  • Thomas S Richardson

Organizations

  • University of Washington

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Covariance
  • Data Analysis
  • Data Science
  • Equations
  • Gaussian Distributions
  • Information Science
  • Markov Models
  • Models
  • Normal Distribution
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Reasoning
  • Statistics

Readers

  • Graph Algorithms and Convex Optimization.
  • Statistical inference.