Learning discrete decomposable graphical models via constraint optimization

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Potsdam
  • Griffith University Queensland
  • University of Helsinki
  • Åbo Akademi University

Abstract

Statistical model learning problems are traditionally solved using either heuristic greedy optimization or stochastic simulation, such as Markov chain Monte Carlo or simulated annealing. Recently, there has been an increasing interest in the use of combinatorial search methods, including those based on computational logic. Some of these methods are particularly attractive since they can also be successful in proving the global optimality of solutions, in contrast to stochastic algorithms that only guarantee optimality at the limit. Here we improve and generalize a recently introduced constraint-based method for learning undirected graphical models. The new method combines perfect elimination orderings with various strategies for solution pruning and offers a dramatic improvement both in terms of time and memory complexity. We also show that the method is capable of efficiently handling a more general class of models, called stratified/labeled graphical models, which have an astronomically larger model space.

Details

Original languageEnglish
Pages (from-to)115-130
JournalSTATISTICS AND COMPUTING
Volume27
Issue number1
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • Answer set programming, Constraint programming, Graphical models, MAXSAT, Satisfiability, Structure learning

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