Log-mean linear models for binary data

Abstract

This paper introduces a novel class of models for binary data, which we call log-mean linear models. The characterizing feature of these models is that they are specified by linear constraints on the log-mean linear parameter, defined as a log-linear expansion of the mean parameter of the multivariate Bernoulli distribution. We show that marginal independence relationships between variables can be specified by setting certain log-mean linear interactions to zero and, more specifically, that graphical models of marginal independence are log-mean linear models. Our approach overcomes some drawbacks of the existing parameterizations of graphical models of marginal independence.

References in corpus (2)

• Marginal log-linear parameters for graphical Markov models
• Two algorithms for fitting constrained marginal models

Cited by in corpus (4)

• Markov Properties of Discrete Determinantal Point Processes
• Dichotomization invariant log-mean linear parameterization for discrete graphical models of marginal independence
• Composite mixture of log-linear models for categorical data
• Random Networks, Graphical Models, and Exchangeability