Statistical analysis with missing data
Statistical analysis with missing data
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Connectionist learning of belief networks
Artificial Intelligence
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Learning Bayesian networks with local structure
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
Learning in graphical models
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Learning Bayesian Networks
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research
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Logistic regression techniques can be used to restrict the conditional probabilities of a Bayesian network for discrete variables. More specifically, each variable of the network can be modeled through a logistic regression model, in which the parents of the variable define the covariates. When all main effects and interactions between the parent variables are incorporated as covariates, the conditional probabilities are estimated without restrictions, as in a traditional Bayesian network. By incorporating interaction terms up to a specific order only, the number of parameters can be drastically reduced. Furthermore, ordered logistic regression can be used when the categories of a variable are ordered, resulting in even more parsimonious models. Parameters are estimated by a modified junction tree algorithm. The approach is illustrated with the Alarm network.