Information Theory, Inference and Learning Algorithms

作者：DavidJ.C.MacKay

出版社：CambridgeUniversityPress

出版年：2003-10-6

页数：640

定价：USD80.00

装帧：Hardcover

ISBN：9780521642989

内容简介
······

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology – communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book 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. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes – the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

作者简介
······

Sir David John Cameron MacKay FRS FInstP FICE (22 April 1967 – 14 April 2016) was a British physicist, mathematician, and academic. He was the Regius Professor of Engineering in the Department of Engineering at the University of Cambridge and from 2009 to 2014 was Chief Scientific Adviser to the UK Department of Energy and Climate Change (DECC). MacKay authored the book Sustainable Energy – Without the Hot Air.

目录
······

1 Introduction to Information Theory

2 Probability, Entropy, and Inference

3 More about Inference

Part I Data Compression

4 The Source Coding Theorem

5 Symbol Codes

6 Stream Codes

7 Codes for Integers

Part II Noisy-Channel Coding

8 Dependent Random Variables

9 Communication over a Noisy Channel

10 The Noisy-Channel Coding Theorem

11 Error-Correcting Codes and Real Channels

Part III Further Topics in Information Theory

12 Hash Codes: Codes for Efficient Information Retrieval

13 Binary Codes

14 Very Good Linear Codes Exist

15 Further Exercises on Information Theory

16 Message Passing

17 Communication over Constrained Noiseless Channels

18 Crosswords and Codebreaking

19 Why have Sex? Information Acquisition and Evolution

Part IV Probabilities and Inference

20 An Example Inference Task: Clustering

21 Exact Inference by Complete Enumeration

22 Maximum Likelihood and Clustering

23 Useful Probability Distributions

24 Exact Marginalization

25 Exact Marginalization in Trellises

26 Exact Marginalization in Graphs

27 Laplace's Method

28 Model Comparison and Occam's Razor

29 Monte Carlo Methods

30 Efficient Monte Carlo Methods

31 Ising Models

32 Exact Monte Carlo Sampling

33 Variational Methods

34 Independent Component Analysis and Latent Variable Modelling

35 Random Inference Topics

36 Decision Theory

37 Bayesian Inference and Sampling Theory

Part V Neural networks

38 Introduction to Neural Networks

39 The Single Neuron as a Classifier

40 Capacity of a Single Neuron

41 Learning as Inference

42 Hopfield Networks

43 Boltzmann Machines

44 Supervised Learning in Multilayer Networks

45 Gaussian Processes

46 Deconvolution

Part VI Sparse Graph Codes

47 Low-Density Parity-Check Codes

48 Convolutional Codes and Turbo Codes

49 Repeat-Accumulate Codes

50 Digital Fountain Codes

Part VII Appendices

Notation; Some Physics; Some Mathematics

评论 ······

早年读的，当时的感觉是深入但不浅出。适合做参考，作主打可能会事倍功半。

有谁一起学习这本书吗？一起讨论吧QQ:63583981

教科书的榜样

:
G201/M153

• 下载电子版：下载地址
• 购买纸质版：京东商城
• 购买纸质版：当当网