A Bayesian Network is a graphical model that represents the probabilistic relationships among a set of variables. It uses a directed acyclic graph (DAG) where each node represents a variable, and the edges indicate conditional dependencies between them. This structure allows for efficient computation of joint probabilities and facilitates reasoning under uncertainty.
Applications/Use Cases:
- Medical Diagnosis: Assessing the likelihood of diseases based on symptoms and test results.
- Risk Assessment: Evaluating potential risks in financial portfolios or engineering systems.
- Gene Regulatory Networks: Modeling interactions between genes to understand biological processes.