Maximum Entropy Modeling: Applications & Inference

This lecture delves into the concept of maximum entropy modeling, focusing on its applications in neuroscience data and protein sequence data. It covers topics such as quantifying randomness, statistical dependence, inferring probability distributions, and finding relevant dimensions in data through dimension reduction techniques like principal component analysis. The lecture also explores the link between maximum entropy modeling and physics, discussing the inference of probability distributions from data and the prediction of protein structure from sequence data.