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SUMMARY:Bayesian network model selection using integer programming - Dr Ja
mes Cussens (University of York)
DTSTART;VALUE=DATE-TIME:20150604T141500
DTEND;VALUE=DATE-TIME:20150604T151500
UID:https://talks.ox.ac.uk/talks/id/36ca2afe-0a29-4e48-8db1-231999afde06/
DESCRIPTION:With complete data and appropriately chosen parameter priors t
he problem of finding a Bayesian network with maximal log marginal likelih
ood (LML) becomes a purely discrete problem: search for a directed acyclic
graph (DAG) with maximal LML. We solve this problem of discrete optimisat
ion using integer linear programming (ILP) with the SCIP (Solving Constrai
nt Integer Programming) framework. In many cases this allows us to solve t
he problem: we find a DAG which we know to have maximal LML. Also using IL
P allows prior knowledge\, such as known conditional independence relation
s\, to be expressed as constraints on DAG structure The key to efficient s
olving is to add certain linear constraints ruling out *cyclic* digraphs d
uring the search. I will report on the successes and limitations of this a
pproach and discuss future directions.\n\nSpeakers:\nDr James Cussens (Uni
versity of York)
LOCATION:1 South Parks Road (Lecture Theatre\, Department of Statistics)\,
1 South Parks Road OX1 3TG
URL:https://talks.ox.ac.uk/talks/id/36ca2afe-0a29-4e48-8db1-231999afde06/
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DESCRIPTION:Talk:Bayesian network model selection using integer programmin
g - Dr James Cussens (University of York)
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