Probabilistic Graphical Models
Probabilistic Graphical Models (PGMs) are a framework for representing complex probability distributions using graphs, where nodes represent random variables and edges represent probabilistic dependencies between them. They combine graph theory and probability theory to provide a compact and interpretable way to model uncertainty and relationships in data. PGMs are widely used in machine learning, artificial intelligence, and statistics for tasks like inference, learning, and decision-making.
Developers should learn PGMs when working on projects involving uncertainty, such as in Bayesian networks for medical diagnosis, Markov random fields for image segmentation, or hidden Markov models for speech recognition. They are essential for building systems that require probabilistic reasoning, such as in natural language processing, computer vision, and robotics, where modeling dependencies and making predictions under uncertainty is critical.