Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian classification (AutoClass): theory and results
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Machine Learning - Special issue on learning with probabilistic representations
Algebraic Analysis for Nonidentifiable Learning Machines
Neural Computation
On the geometry of Bayesian graphical models with hidden variables
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Upper bound for variational free energy of Bayesian networks
Machine Learning
Automatic Boosting of Cross-Product Coverage Using Bayesian Networks
HVC '08 Proceedings of the 4th International Haifa Verification Conference on Hardware and Software: Verification and Testing
Effective dimensions of hierarchical latent class models
Journal of Artificial Intelligence Research
Marginal Likelihood Integrals for Mixtures of Independence Models
The Journal of Machine Learning Research
The Journal of Machine Learning Research
The Journal of Machine Learning Research
An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models
The Journal of Machine Learning Research
A widely applicable Bayesian information criterion
The Journal of Machine Learning Research
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We develop a closed form asymptotic formula to compute the marginal likelihood of data given a naive Bayesian network model with two hidden states and binary features. This formula deviates from the standard BIC score. Our work provides a concrete example that the BIC score is generally incorrect for statistical models that belong to stratified exponential families. This claim stands in contrast to linear and curved exponential families, where the BIC score has been proven to provide a correct asymptotic approximation for the marginal likelihood.