Learning Chordal Markov Networks by Constraint Satisfaction
This provides a more efficient and exact method for learning Markov networks, which is incremental as it builds on existing solver technology.
The paper tackles the problem of learning Markov network structures from data by formulating it as a constraint satisfaction problem, enabling the use of solvers to compute optimal networks and proving optimality for structures previously found by stochastic search.
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.