Naive Bayes models for probability estimation

13 years 11 days ago
Naive Bayes models for probability estimation
Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating and computing arbitrary joint, conditional and marginal distributions). In this paper we show that, for a wide range of benchmark datasets, naive Bayes models learned using EM have accuracy and learning time comparable to Bayesian networks with context-specific independence. Most significantly, naive Bayes inference is orders of magnitude faster than Bayesian network inference using Gibbs sampling and belief propagation. This makes naive Bayes models a very attractive alternative to Bayesian networks for general probability estimation, particularly in large or real-time domains.
Daniel Lowd, Pedro Domingos
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2005
Where ICML
Authors Daniel Lowd, Pedro Domingos
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