# Learning Chordal Markov Networks by Constraint Satisfaction

arXiv: https://arxiv.org/abs/1310.0927 · HTML: https://mlsanity.com/paper/1310.0927/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2013.

## Built on by (most cited first, 4 of 4)

- [Propositional Encodings of Acyclicity and Reachability by using Vertex Elimination](https://mlsanity.com/paper/2105.12908/) (2021)
- [Argumentative Causal Discovery](https://mlsanity.com/paper/2405.11250/) (2024)
- [Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood](https://mlsanity.com/paper/2103.15540/) (2021)
- [Statistical relational learning and neuro-symbolic AI: what does first-order logic offer?](https://mlsanity.com/paper/2306.13660/) (2023)

## Abstract (arXiv)

We investigate the problem of learning the structure of a Markov network from data. It is shown that the structure of such networks can be described in terms of constraints which enables the use of existing solver technology with optimization capabilities to compute optimal networks starting from initial scores computed from the data. To achieve efficient encodings, we develop a novel characterization of Markov network structure using a balancing condition on the separators between cliques forming the network. The resulting translations into propositional satisfiability and its extensions such as maximum satisfiability, satisfiability modulo theories, and answer set programming, enable us to prove optimal certain network structures which have been previously found by stochastic search.