Graphical Markov models: overview

TL;DR

This paper provides an overview of graphical Markov models, highlighting their historical development, core concepts, and applications in analyzing longitudinal data and developmental pathways.

Contribution

It offers a comprehensive summary of the evolution, key concepts, and recent advances in graphical Markov models over the past 40 years.

Findings

Sequences of regressions are effective for longitudinal data analysis.

Regression graphs help trace developmental pathways.

Recent results enhance understanding of complex data structures.

Abstract

We describe how graphical Markov models started to emerge in the last 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 best suited for analyzing longitudinal data and for tracing developmental pathways. Interpretations are illustrated using two sets of data and some of the more recent, important results for sequences of regressions are summarized.