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 alongsi...
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
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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 Sustain...
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
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(更多)
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
· · · · · · (收起)
Probabilities can be used in two ways.
1. Probabilities can describe frequencies of outcomes in random experiments
2. Probabilities can also be used, more generally, to describe degrees of belief in propositions that do not involve random variables
This more general use of probability to quantify beliefs is known as the Bayesian viewpoint. It is also known as the subjective interpretation of probability, since the probabilities depend on assumptions.
Advocates of a Bayesian approach to data modelling and pattern recognition do not view this subjectivity as a defect, since in their view,
you cannot do inference without making assumptions. (查看原文)
The details of the other possible outcomes and their probabilities are irrelevant.
All that matters is the probability of the outcome that actually
happened (here, that the ball drawn was black) given the dierent hypotheses.
We need only to know the likelihood, i.e., how the probability of the data
that happened varies with the hypothesis. This simple rule about inference is
known as the likelihood principle.
The likelihood principle: given a generative model for data d given
parameters [;\theta;], [;P(d|\theta);], and having observed a particular outcome
[;d_1;], all inferences and predictions should depend only on the function
[;P(d_1|\theta);].
In spite of the simplicity of this principle, many classical statistical methods
violate it. (查看原文)
1.刚从图书馆借到这本书,顺着书中的支持网站,发现作者把公开课视频也免费放到网上了,还可以直接下到英文原版电子版,这是什么精神~ ”A series of sixteen lectures covering the core of the book "Information Theory, Inference, and Learning Algorithms (Cambridge Un...
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这一段内容是讲概率论的两种解释:贝叶斯学派和非贝叶斯学派。 概率的常规解释为随机试验中的频率,但是贝叶斯学派的观点拓广了概率的应用范围,概率还可以表示对命题的信服(belief)程度,因为这样的概率基于一些假设(assumption),所以,对概率的解释增加了主观性。 Probabilities can be used in two ways. 1. Probabilities can describe frequencies of outcomes in random experiments 2. Probabilities can also be u...
Probabilities can be used in two ways.
1. Probabilities can describe frequencies of outcomes in random experiments
2. Probabilities can also be used, more generally, to describe degrees of belief in propositions that do not involve random variables
This more general use of probability to quantify beliefs is known as the Bayesian viewpoint. It is also known as the subjective interpretation of probability, since the probabilities depend on assumptions.
Advocates of a Bayesian approach to data modelling and pattern recognition do not view this subjectivity as a defect, since in their view,
you cannot do inference without making assumptions.引自 2.2 The meaning of probability
a disk drive is an example of communication, which doesn't have to involve information going from one place to another. When we write a file on a disk drive, we'll read it off in the same location - but at a later time.
2020-06-20 22:13:41
a disk drive is an example of communication, which doesn't have to involve information going from one place to another.
When we write a file on a disk drive, we'll read it off in the same location - but at a later time.
这一段内容是讲概率论的两种解释:贝叶斯学派和非贝叶斯学派。 概率的常规解释为随机试验中的频率,但是贝叶斯学派的观点拓广了概率的应用范围,概率还可以表示对命题的信服(belief)程度,因为这样的概率基于一些假设(assumption),所以,对概率的解释增加了主观性。 Probabilities can be used in two ways. 1. Probabilities can describe frequencies of outcomes in random experiments 2. Probabilities can also be u...
Probabilities can be used in two ways.
1. Probabilities can describe frequencies of outcomes in random experiments
2. Probabilities can also be used, more generally, to describe degrees of belief in propositions that do not involve random variables
This more general use of probability to quantify beliefs is known as the Bayesian viewpoint. It is also known as the subjective interpretation of probability, since the probabilities depend on assumptions.
Advocates of a Bayesian approach to data modelling and pattern recognition do not view this subjectivity as a defect, since in their view,
you cannot do inference without making assumptions.引自 2.2 The meaning of probability
a disk drive is an example of communication, which doesn't have to involve information going from one place to another. When we write a file on a disk drive, we'll read it off in the same location - but at a later time.
2020-06-20 22:13:41
a disk drive is an example of communication, which doesn't have to involve information going from one place to another.
When we write a file on a disk drive, we'll read it off in the same location - but at a later time.
Shannon 1948: fundamental problem "reliable communication over an unreliable channel" received signal ~= transmitted signal + noise we would like "received message = transmitted message" solutions: 1. physical solutions; 2. system solutions source message->ENCODER->coded transmission->CHANNEL->received message->DECODER->predicted s
2013-10-25 01:56:47
Shannon 1948: fundamental problem "reliable communication over an unreliable channel"
received signal ~= transmitted signal + noise
we would like "received message = transmitted message"
solutions: 1. physical solutions; 2. system solutions
source message->ENCODER->coded transmission->CHANNEL->received message->DECODER->predicted s
0 有用 log1302 2018-07-20 10:33:21
好书👍圈粉
0 有用 英子 2010-07-07 11:34:48
有点难,但是我觉得写的挺好的。
12 有用 阅微草堂 2016-02-03 11:38:50
信息论和机器学习是一个硬币的两面。传统的信息论两条理论上的香农,工程应用是通信。具体的:贝叶斯数据模型,蒙特卡洛,变分法,聚类算法,神经网络。大脑是压缩和通信系统
0 有用 K42 2022-01-20 07:38:17
需要买一本 反复查阅
0 有用 蝉 2013-11-28 17:47:27
: G201/M153
0 有用 K42 2022-01-20 07:38:17
需要买一本 反复查阅
0 有用 Abacus 2021-12-20 02:55:04
很清晰简明的信息论教程,非常适合初学者的学习
0 有用 润Rhuen 2021-08-14 19:28:38
一半弃读
0 有用 δεζη 2021-03-20 15:12:39
(读过部分章节)与很多教材不同的是,把很多东西放在一起讨论,很有意思。 适合做个补充类读物。要是学信息论或者机器学习还是以其他教材为主吧
0 有用 NumineViget 2020-03-27 17:49:48
Shannon真的是我男神。很美妙的一套体系,日常查阅必备。有空可以深入读读,会对一些看似莫名其妙的 log 们有更深的体会。