A Markov Random Field (MRF) is a mathematical model used to represent the joint distribution of a set of random variables with a specific structure. It is particularly useful in fields like computer vision and statistical physics. In an MRF, the variables are arranged in a graph where each node represents a variable, and edges indicate dependencies between them. The key property is that a variable is conditionally independent of all other variables given its neighbors. MRFs are often employed in image processing tasks, such as image segmentation and texture synthesis. They allow for the modeling of spatial relationships, enabling the capture of local patterns and interactions. By using MRFs, one can effectively infer the state of a system based on observed data while considering the influence of neighboring variables.