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  • Learning discrete decomposable graphical models via constraint optimization

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    RintanenPUB842.pdf (237.4Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Janhunen, Tomi
    Gebser, Martin
    Rintanen, Jussi
    Nyman, Henrik
    Pensar, Johan
    Corander, Jukka
    Griffith University Author(s)
    Rintanen, Jussi
    Year published
    2015
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    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 ...
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    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.
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    Journal Title
    Statistics and Computing
    DOI
    https://doi.org/10.1007/s11222-015-9611-4
    Copyright Statement
    © 2007 Springer US. This is an electronic version of an article published in Statistics and Computing, January 2017, Volume 27, Issue 1, pp 115–130. Statistics and Computing is available online at: http://link.springer.com/ with the open URL of your article.
    Note
    This publication has been entered into Griffith Research Online as an Advanced Online Version.
    Subject
    Computation Theory and Mathematics not elsewhere classified
    Statistics
    Computation Theory and Mathematics
    Publication URI
    http://hdl.handle.net/10072/101831
    Collection
    • Journal articles

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