**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

**Entropy and Information Theory**

by

**Robert M. Gray**-

**Springer**

The book covers the theory of probabilistic information measures and application to coding theorems for information sources and noisy channels. This is an up-to-date treatment of traditional information theory emphasizing ergodic theory.

(

**12205**views)

**Error-Correction Coding and Decoding**

by

**Martin Tomlinson, et al.**-

**Springer**

This book discusses both the theory and practical applications of self-correcting data, commonly known as error-correcting codes. The applications included demonstrate the importance of these codes in a wide range of everyday technologies.

(

**2017**views)

**Lecture Notes on Network Information Theory**

by

**Abbas El Gamal, Young-Han Kim**-

**arXiv**

Network information theory deals with the fundamental limits on information flow in networks and optimal coding and protocols. These notes provide a broad coverage of key results, techniques, and open problems in network information theory.

(

**9747**views)

**Information Theory, Excess Entropy and Statistical Complexity**

by

**David Feldman**-

**College of the Atlantic**

This e-book is a brief tutorial on information theory, excess entropy and statistical complexity. From the table of contents: Background in Information Theory; Entropy Density and Excess Entropy; Computational Mechanics.

(

**8435**views)