PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Factorized NML models
Petri Myllymäki, Teemu Roos, Tomi Silander, Petri Kontkanen and Henry Tirri
In: Festschrift in Honor of Jorma Rissanen on the Occasion of his 75th Birthday TICSP series , 38 . (2008) Tampere International Center for Signal Processing , Tampere, Finland , pp. 189-204. ISBN 978 952 15 1962 8


We consider probabilistic graphical models where a directed acyclic graph represents a factorization of a joint probability distribution: the joint probability of the variables is represented as a product of conditional probabilities, one for each variable conditioned on its immediate parents in the graph. For this type of models, computing the normalized maximum likelihood (NML) is computationally very demanding. We suggest a computationally feasible alternative to NML, the factorized NML, where the normalization is done locally for each conditional distribution, and not globally.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:5163
Deposited By:Teemu Roos
Deposited On:24 March 2009