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.

How has Bayesian Modeling and Causal Inference's publication output changed over time?

Output grew0% over the shown period — from 1 works in 1994 to 1 in 2023.

What are the most-cited papers on Bayesian Modeling and Causal Inference?

Where is Bayesian Modeling and Causal Inference research published, and who funds it?

How much of the research on Bayesian Modeling and Causal Inference is open access?