Bayesian Modeling and Causal Inference

Bayesian Modeling and Causal Inference is a research topic within Artificial Intelligence. Science Explorer counts 22k research works in it since 1950. 10.0% of them reached the world's top 10% most cited for their field and year.

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.

• Bayesian Networks
• Causal Inference
• Graphical Models
• Probabilistic Learning
• Markov Logic Networks
• Inference Algorithms
• Causal Discovery
• Probabilistic Graphical Models
• Structure Learning
• Imprecise Probabilities