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Importance Sampling for General Hybrid Bayesian Networks
2007
Journal of machine learning research
Some real problems are more naturally modeled by hybrid Bayesian networks that consist of mixtures of continuous and discrete variables with their interactions described by equations and continuous probability distributions. However, inference in such general hybrid models is hard. Therefore, existing approaches either only deal with special instances, such as Conditional Linear Gaussians (CLGs), or approximate a general model with a restricted version and then perform inference on the simpler
dblp:journals/jmlr/YuanD07
fatcat:omevadxlt5ck7efb3ore5wko44