# Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood

arXiv: https://arxiv.org/abs/2103.15540 · HTML: https://mlsanity.com/paper/2103.15540/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 29 Mar 2021.

## Builds on (most cited first, 10 of 10)

- [Context-Specific Independence in Bayesian Networks](https://mlsanity.com/paper/1302.3562/) (2013)
- [A Bayesian Approach to Learning Bayesian Networks with Local Structure](https://mlsanity.com/paper/1302.1528/) (2013)
- [Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models](https://mlsanity.com/paper/1310.1187/) (2013)
- [Learning Bayesian Networks with Local Structure](https://mlsanity.com/paper/1302.3577/) (2013)
- [Stratified Graphical Models - Context-Specific Independence in Graphical Models](https://mlsanity.com/paper/1309.6415/) (2013)
- [Learning Chordal Markov Networks by Constraint Satisfaction](https://mlsanity.com/paper/1310.0927/) (2013)
- [Marginal Pseudo-Likelihood Learning of Markov Network structures](https://mlsanity.com/paper/1401.4988/) (2014)
- [Context-specific independence in graphical log-linear models](https://mlsanity.com/paper/1409.2713/) (2014)
- [Linear and Parallel Learning of Markov Random Fields](https://mlsanity.com/paper/1308.6342/) (2013)
- [Marginal and simultaneous predictive classification using stratified graphical models](https://mlsanity.com/paper/1401.8078/) (2014)

## Built on by (most cited first, 2 of 2)

- [Staged trees and asymmetry-labeled DAGs](https://mlsanity.com/paper/2108.01994/) (2021)
- [Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear](https://mlsanity.com/paper/2301.00629/) (2023)

## Abstract (arXiv)

Markov networks are popular models for discrete multivariate systems where the dependence structure of the variables is specified by an undirected graph. To allow for more expressive dependence structures, several generalizations of Markov networks have been proposed. Here we consider the class of contextual Markov networks which takes into account possible context-specific independences among pairs of variables. Structure learning of contextual Markov networks is very challenging due to the extremely large number of possible structures. One of the main challenges has been to design a score, by which a structure can be assessed in terms of model fit related to complexity, without assuming chordality. Here we introduce the marginal pseudo-likelihood as an analytically tractable criterion for general contextual Markov networks. Our criterion is shown to yield a consistent structure estimator. Experiments demonstrate the favorable properties of our method in terms of predictive accuracy of the inferred models.