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
· · · · · · (收起)
具體描述
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.
用戶評價
##一半棄讀
評分##有點難，但是我覺得寫的挺好的。
評分##作者Mackay，要記住的。買瞭一本中文的，要中英文對照著讀。這本書是真是練習內功呀。
評分##感覺有時間慢慢啃的話肯定能打開很多新世界大門
評分##“ this exciting textbook”....纔知道MacKay去世瞭，可惜啊。
評分##有點難，但是我覺得寫的挺好的。
評分##“ this exciting textbook”....纔知道MacKay去世瞭，可惜啊。
評分##早年讀的，當時的感覺是深入但不淺齣。適閤做參考，作主打可能會事倍功半。
評分##有點難，但是我覺得寫的挺好的。
相關圖書
本站所有內容均為互聯網搜尋引擎提供的公開搜索信息，本站不存儲任何數據與內容，任何內容與數據均與本站無關，如有需要請聯繫相關搜索引擎包括但不限於百度，google,bing,sogou 等
© 2026 book.teaonline.club All Rights Reserved. 圖書大百科 版權所有