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Gaussian Mixture Reduction for Time-Constrained Approximate Inference in Hybrid Bayesian Networks
2019
Applied Sciences
Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variables have Gaussian distributions and all children of continuous random variables must be continuous. Inference in CG networks can be NP-hard even for
doi:10.3390/app9102055
fatcat:c3vocssyxvb6nerw3itdbc624m