Physical Sciences → Computer Science → Artificial Intelligence
Bayesian Modeling and Causal Inference
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.
56.5K works worldwide1.1M citations
Bayesian NetworksCausal InferenceGraphical ModelsProbabilistic LearningMarkov Logic NetworksInference AlgorithmsCausal DiscoveryProbabilistic Graphical ModelsStructure LearningImprecise Probabilities
Journals publishing in this area
3
Journal of the Royal Statistical Society Series B (Statistical Methodology)
ISSN 1369-7412284 articles in this topic
307h-index
1.61Impact
4.2KArticles
739.3KCitations
6
264h-index
2.13Impact
11.4KArticles
500KCitations