2018/12/23 by Robert Leppert, Leppert, Robert, Karl-Heinz Zimmermann +1
Computer Science · Mathematics · #16Y60 #62F15 #68T05 #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Data Management and Algorithms #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and Algorithms #cs.AI #msc:16Y60 #msc:62F15 #msc:68T05 #stat.ML
paper · pdf · doi:10.48550/arxiv.1901.01837
16 pages
arxiv created 2018/12/23 · openalex publication_date 2018/12/23 · arxiv updated 2019/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Machine learning provides algorithms that can learn from data and make inferences or predictions on data. Bayesian networks are a class of graphical models that allow to represent a collection of random variables and their condititional dependencies by directed acyclic graphs. In this paper, an inference algorithm for the hidden random variables of a Bayesian network is given by using the tropicalization of the marginal distribution of the observed variables. By restricting the topological structure to graded networks, an inference algorithm for graded Bayesian networks will be established that evaluates the hidden random variables rank by rank and in this way yields the most probable states of the hidden variables. This algorithm can be viewed as a generalized version of the Viterbi algorithm for graded Bayesian networks.