A variational approximation for Bayesian networks with discrete and continuous latent variables
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Exact inference in networks with discrete children of continuous parents
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Implementation of continuous Bayesian networks using sums of weighted Gaussians
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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This paper proposes a general formalism for evaluating hybrid Bayesian networks. The formalism approximates a hybrid Bayesian network into the form, called fuzzy partial least-squares Bayesian network (FPBN). The form replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adding a directed link from that partner discrete variable to the continuous one. The partner discrete variable is acquired by the discretization of the original continuous variable with a fuzzification algorithm based on the structure adaptive-tuning neural network model. In addition, the dependence between the partner discrete variable and the original continuous variable is approximated by fuzzy sets, and the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution in which partial least-squares (PLS) is proposed as an alternative method for computing the vector of regression parameter. The experimental results are included to demonstrate the performances of the new approach.