Bayesian Networks
Overview
A Bayesian network uses a directed acyclic graph and local conditional probability models to represent how variables depend on one another, supporting tasks such as diagnosis, prediction, and information gathering. Researchers learn graphs from data using score-based and constraint-based search, often adding expert knowledge or priors, while inference methods range from exact algorithms to sampling and neural approximations. Applications span causal discovery, clinical modeling, time series, and synthetic data generation, with growing work combining networks with language models and differentiable mechanisms. Key challenges include scaling inference and structure search, distinguishing causal direction from observational association, and handling latent confounding, sparse data, and uncertain or imperfect prior knowledge.
Latest papers
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