Context-specific independencies in stratified chain regression graphical models

Identifying Hidden Variables from Context-Specific Independencies

Learning a Bayesian network from data is a model specific task, and thus requires careful consideration of contextual information, namely, contextual independencies. In this paper, we study the role of hidden variables in learning causal models from data. We show how statistical methods can help us discover these hidden variables. We suggest hidden variables are wrongly ignored in inference, be...

Graphical simulation using chain models

This paper presents an extension of a previously described framework for the speci cation and manipulation of models of systems. This extension is particularly useful in the context of graphical simulation. The original framework and its extension include a convenient Application Programming Interface (API), and this paper provides examples of the use of this API in the context of graphical sim...

Tree-Based Models for Fiting Stratified Linear Regression Models

This paper generalizes the methods developed in Shannon, Province and Rao (2001) to use recursive partitioning to identify subsets of the aggregate data within each of which simple linear regression models give better fit. This method is proposed as an alternative to multivariate regression modeling when the analyst is primarily concerned with the regression of an outcome onto a single predicto...

Discriminative Graphical Models for Context-Based Classification

Graphical Models for Relations - Modeling Relational Context

We derive a multinomial sampling model for analyzing the relationships between two or more entities. The parameters in the multinomial model are derived from factorizing multi-way contingency tables. We show how contextual information can be included and propose a graphical representation of model dependencies. The graphical representation allows us to decompose a multivariate domain into inter...