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Publicatie
First-order Bayes-ball
Boekbijdrage - Boekhoofdstuk Conferentiebijdrage
Efficient probabilistic inference is key to the success of statistical relational learning. One issue that increases the cost of inference is the presence of irrelevant random variables. The Bayes-ball algorithm can identify the requisite variables in a propositional Bayesian network and thus ignore irrelevant variables. This paper presents a lifted version of Bayes-ball, which works directly on the first-order level, and shows how this algorithm applies to (lifted) inference in directed first-order probabilistic models.
Boek: Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010
Pagina's: 369 - 384
ISBN:364215882X
Jaar van publicatie:2010
BOF-keylabel:ja
IOF-keylabel:ja
Authors from:Higher Education
Toegankelijkheid:Open