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Graphical Markov Models: Overview
Abstract
We describe how graphical Markov models emerged in the past 40. years, based on three essential concepts that had been developed independently more than a century ago. Sequences of joint or single regressions and their regression graphs are singled out as being the subclass that is best suited for analyzing longitudinal data and for tracing developmental pathways, both in observational and in intervention studies. Interpretations are illustrated using two sets of data. Furthermore, some of the more recent, important results for sequences of regressions are summarized- Media Engineering
- Bioinformatics and Systems Biology
- Probability Theory and Statistics
- Independence-predicting graphs
- Observational studies
- Regression graphs
- Direct confounding
- Dependence-inducing distributions
- Intersection property
- Issues of causality
- Longitudinal studies
- Intervention studies
- Conditional independence
- Composition property
- Markov equivalence
- Independence-preserving graphs
- Separation criteria
- Indirect confounding
Similar works
This paper was published in Chalmers Research.
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