@inproceedings{e968c666c0644b7abd22e6248585031e,
title = "Iterative conditional fitting for discrete chain graph models",
abstract = "'Iterative conditional fitting' is a recently proposed algorithm that can be used for maximization of the likelihood function in marginal independence models for categorical data. This paper describes a modification of this algorithm, which allows one to compute maximum likelihood estimates in a class of chain graph models for categorical data. The considered discrete chain graph models are defined using conditional independence relations arising in recursive multivariate regressions with correlated errors. This Markov interpretation of the chain graph is consistent with treating the graph as a path diagram and differs from other interpretations known as the LWF and AMP Markov properties.",
keywords = "Categorical data, Chain graph, Conditional independence, Graphical model",
author = "Mathias Drton",
year = "2008",
doi = "10.1007/978-3-7908-2084-3_8",
language = "English",
isbn = "9783790820836",
series = "COMPSTAT 2008 - Proceedings in Computational Statistics, 18th Symposium",
publisher = "Springer Berlin Heidelberg",
pages = "93--104",
booktitle = "COMPSTAT 2008 - Proceedings in Computational Statistics, 18th Symposium",
note = "18th Symposium on Computational Statistics, COMPSTAT 2008 ; Conference date: 24-08-2008 Through 29-08-2008",
}