Probabilistic Graphical Model

Probabilistic graphical models (PGMs) are statistical frameworks representing complex systems through graphs where nodes denote variables and edges encode probabilistic dependencies. Current research emphasizes efficient inference algorithms, particularly for large-scale models and those incorporating latent variables, with a focus on improving scalability and accuracy through techniques like lifted inference, variational methods, and the integration of deep learning architectures. PGMs find broad application in diverse fields, from causal inference and knowledge graph analysis to semantic communication and anomaly detection, offering powerful tools for modeling uncertainty and extracting insights from data.