We present a new method to propagate lower bounds on conditional probability distributions in conventional Bayesian networks. Our method guarantees to provide outer approximations of the exact lower bounds. A key advantage is that we can use any available algorithms and tools for Bayesian networks in order to represent and infer lower bounds. This new method yields results that are provable exact for trees with binary variables, and results which are competitive to existing approximations in credal networks for all other network structures. Our method is not limited to a specific kind of network structure. Basically, it is also not restricted to a specific kind of inference, but we restrict our analysis to prognostic inference in this article. The computational complexity is superior to that of other existing approaches.
Fast Parallel Exact Inference on Bayesian Networks: Poster
The University of Western Australia（西澳大利亚大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Hong Kong University of Science and Technology（香港科技大学）
Bayesian inference for data-efficient, explainable, and safe robotic motion planning: A review
University of Eastern Finland（东芬兰大学）；School of Computing（计算机学院）；Shenzhen Technology University（深圳技术大学）；College of Big Data and Internet（大数据与互联网学院）；Chengdu University of Information Technology（成都信息工程大学）；College of Blockchain industry（区块链产业学院）；College of Health Science and Environmental Engineering（健康科学与环境工程学院）
Spintronics based Stochastic Computing for Efficient Bayesian Inference System
Fert Beijing Research Institute, BDBC, School of Electronic and Information Engineering（费尔北京研究院，大数据科学与脑机智能高精尖创新中心，电子信息工程学院）；Fert Beijing Research Institute, BDBC, School of Computer Science and Engineering（费尔北京研究院，大数据科学与脑机智能高精尖创新中心，计算机科学与工程学院）；Department of Electrical and Computer Engineering, Duke University（杜克大学电气与计算机工程系）；Beihang University（北京航空航天大学）
