2013/02/20 by David Heckerman, Heckerman, David, Dan Geiger +1 · 6 citations
Computer Science · Decision Sciences · Mathematics · #Algorithm #Applied mathematics #Artificial Intelligence (cs.AI) #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Bayesian inference #Bayesian network #Bayesian probability #Census and Population Estimation #Computer science #Data Quality and Management #Dirichlet distribution #Econometrics #FOS: Computer and information sciences #G.3 #Gaussian #Gaussian process #I.2 #Machine learning #Mathematical economics #Mathematics #Metric (unit) #Multivariate statistics #Physics #Quantum mechanics #Statistical physics #Unification #Variable-order Bayesian network #Wishart distribution #cs.AI
paper · pdf · doi:10.48550/arxiv.1302.4957
published in arXiv (Cornell University), 274-284 (Cornell University) · This version has improved pointers to the literature
openalex publication_date 2013/02/20 · arxiv created 2021/06/29 · arxiv updated 2021/07/01 · openalex created_date 2025/10/24 · openalex updated_date 2026/07/28
We examine Bayesian methods for learning Bayesian networks from a combination\nof prior knowledge and statistical data. In particular, we unify the approaches\nwe presented at last year's conference for discrete and Gaussian domains. We\nderive a general Bayesian scoring metric, appropriate for both domains. We then\nuse this metric in combination with well-known statistical facts about the\nDirichlet and normal--Wishart distributions to derive our metrics for discrete\nand Gaussian domains.\n