Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Bayesian Network Modeling of Hangul Characters for On-line Handwriting Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Generation of Handwritten Characters with Bayesian network based On-line Handwriting Recognizers
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Utilization of Hierarchical, Stochastic Relationship Modeling for Hangul Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Using test plans for Bayesian modeling
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Spatiotemporal Models for Data-Anomaly Detection in Dynamic Environmental Monitoring Campaigns
ACM Transactions on Sensor Networks (TOSN)
Extending probLog with continuous distributions
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
3D space handwriting recognition with ligature model
UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
Inference in probabilistic logic programs with continuous random variables
Theory and Practice of Logic Programming
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We survey the literature on methods for inference and learning in Bayesian Networks composed of discrete and continuous nodes, in which the continuous nodes have a multivariate Gaussian distribution, whose mean and variance depends on the values of the discrete nodes. We also briefly consider hybrid Dynamic Bayesian Networks, an extension of switching Kalman filters. This report is meant to summarize what is known at a sufficient level of detail to enable someone to implement the algorithms, but without dwelling on formalities.