Graphical models

Graphical models are visual representations used to describe complex relationships among variables in a systematic way. They consist of nodes (which represent variables) connected by edges (which depict relationships or dependencies). This structure helps to illustrate how different factors influence one another, making it easier to analyze and understand data. Commonly used in statistics, machine learning, and artificial intelligence, graphical models facilitate reasoning about uncertainty and enable predictions by capturing the essence of real-world problems. Examples include Bayesian networks and Markov random fields, both of which help in various applications like diagnosis, recommendation systems, and risk assessment.