Information Theory, Inference and Learning Algorithms

Subjects

Description

A comprehensive and intuitive introduction to information theory, Bayesian inference, probabilistic modeling, and machine learning. David MacKay connects concepts such as entropy, probability, coding, neural networks, graphical models, Monte Carlo methods, and error-correcting codes, showing how they arise from common principles of inference and information. The book combines mathematical theory with practical examples and computational experiments.