## AbstractWe 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.
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