Bayesian Modeling and Causal Inference
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Scope of this subject
This cluster of papers focuses on the learning, inference, and applications of Bayesian networks and related probabilistic graphical models. It covers topics such as causal inference, graphical model structure learning, Markov logic networks, and the use of imprecise probabilities in modeling. The papers also discuss various algorithms for probabilistic learning and highlight the applications of Bayesian networks in diverse fields such as ecology, healthcare, and decision making under uncertainty.
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Research vocabulary
Bayesian networks; probabilistic graphical models; causal discovery; Bayesian network structure learning; conditional independence; structure learning; probabilistic inference; imprecise probability; approximate inference; parameter learning; conditional independence testing; influence diagrams; causal learning; exact inference; Bayesian network learning; conditional probability tables; structural causal models; Bayesian network inference; causal judgment; causal Bayesian networks; PC algorithm; Bayesian network classifiers; statistical relational learning; d-separation; undirected graphical models
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No owner-reviewed answer has been published for this exact subject page. This is a gap in this collection; it does not establish that the answer is unavailable elsewhere.
Numeric address and provenance
Morton address: 36183004611218651 · morton4x16-v1
Coordinates: (3, 17, 1702, 11303). The address interleaves categorical identifier bits. Nearby addresses are not guaranteed to describe semantically similar subjects.
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