Learning symmetric causal independence models

DOI10.1007/s10994-007-5041-7zbMath1470.68125OpenAlexW2080221965MaRDI QIDQ1009256

Tom Heskes, Rasa Jurgelenaite

Publication date: 31 March 2009

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-007-5041-7

zbMATH Keywords

Bayesian networksEM-algorithmcausal independence

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)

Related Items (2)

Structural learning of mixed noisy-or Bayesian networks ⋮ Probabilistic inference with noisy-threshold models based on a CP tensor decomposition

Cites Work

• The area above the ordinal dominance graph and the area below the receiver operating characteristic graph
• Bayesian network modelling through qualitative patterns
• An approximation theorem for the Poisson binomial distribution
• The Meaning of Binomial Distribution
• Weighted finite population sampling to maximize entropy
• Noisy-or classifier
• On the Distribution of the Number of Successes in Independent Trials
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Learning symmetric causal independence models