2012/07/04 by Vibhav Gogate, Gogate, Vibhav, Rina Dechter +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences #Logic, Reasoning, and Knowledge #Machine Learning and Algorithms #cs.AI
paper · pdf · doi:10.48550/arxiv.1207.1385
Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)
arxiv created 2012/07/04 · openalex publication_date 2012/07/04 · arxiv updated 2012/07/09 · openalex created_date 2022/09/04 · openalex updated_date 2026/07/28
In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of constraints. We present two approximate inference algorithms for HMNs that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling and Constraint Propagation to address the complexity of modeling and reasoning in HMNs. We demonstrate the performance of our approximate inference algorithms on randomly generated HMNs.