**Information Theory, Inference, and Learning Algorithms**

by David J. C. MacKay

**Publisher**: Cambridge University Press 2003**ISBN/ASIN**: 0521642981**ISBN-13**: 9780521642989**Number of pages**: 640

**Description**:

Information theory and inference, often taught separately, are here united in one entertaining textbook. These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks.

Download or read it online for free here:

**Download link**

(multiple formats)

## Similar books

**Generalized Information Measures and Their Applications**

by

**Inder Jeet Taneja**-

**Universidade Federal de Santa Catarina**

Contents: Shannon's Entropy; Information and Divergence Measures; Entropy-Type Measures; Generalized Information and Divergence Measures; M-Dimensional Divergence Measures and Their Generalizations; Unified (r,s)-Multivariate Entropies; etc.

(

**5190**views)

**A primer on information theory, with applications to neuroscience**

by

**Felix Effenberger**-

**arXiv**

This chapter is supposed to give a short introduction to the fundamentals of information theory, especially suited for people having a less firm background in mathematics and probability theory. The focus will be on neuroscientific topics.

(

**3254**views)

**Information Theory and Statistical Physics**

by

**Neri Merhav**-

**arXiv**

Lecture notes for a graduate course focusing on the relations between Information Theory and Statistical Physics. The course is aimed at EE graduate students in the area of Communications and Information Theory, or graduate students in Physics.

(

**7160**views)

**A Short Course in Information Theory**

by

**David J. C. MacKay**-

**University of Cambridge**

This text discusses the theorems of Claude Shannon, starting from the source coding theorem, and culminating in the noisy channel coding theorem. Along the way we will study simple examples of codes for data compression and error correction.

(

**7433**views)