Bayesian Modeling and Causal Inference

Über 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.

Schlagwörter

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

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