ISTA 311: Foundation of Information and Inference

An introduction to the mathematical theories of probability and information as tools for inference, decision-making, and efficient communication. Topics include discrete and continuous random variables, measures of information and uncertainty, discrete time/discrete state Markov chains, elements of Bayesian inference and decision-making, Bayesian and Maximum Likelihood parameter estimation, and elementary coding theory.

Course Credits
3