Information Theory, Inference, and Learning Algorithms

shawn_yangjx · 发表于 2006-7-30 16:23:29

马上注册，结交更多好友，享用更多功能，让你轻松玩转社区。

您需要登录才可以下载或查看，没有账号？注册

x

Information Theory, Inference, and Learning Algorithms

David J.C. MacKay

Information Theory,
Inference,
and Learning Algorithms
David J.C. MacKay
mackay@mrao.cam.ac.uk

shawn_yangjx · 发表于 2006-7-30 16:24:07

Information Theory, Inference, and Learning Algorithms

David J.C. MacKay

Information Theory,
Inference,
and Learning Algorithms
David J.C. MacKay
mackay@mrao.cam.ac.uk

shawn_yangjx · 发表于 2006-7-30 16:24:43

Information Theory, Inference, and Learning Algorithms

David J.C. MacKay

Information Theory,
Inference,
and Learning Algorithms
David J.C. MacKay
mackay@mrao.cam.ac.uk

shawn_yangjx · 发表于 2006-7-30 16:26:04

Information Theory, Inference, and Learning Algorithms

David J.C. MacKay

Information Theory,
Inference,
and Learning Algorithms
David J.C. MacKay
mackay@mrao.cam.ac.uk

shawn_yangjx · 发表于 2006-7-30 16:27:17

Information Theory, Inference, and Learning Algorithms

David J.C. MacKay

Information Theory,
Inference,
and Learning Algorithms
David J.C. MacKay
mackay@mrao.cam.ac.uk

kuan · 发表于 2007-1-26 08:21:04

感謝分享

ntynty · 发表于 2007-2-10 14:04:56

thanks

houhou1234 · 发表于 2007-2-15 10:32:48

顶起来谢谢

nikka · 发表于 2007-2-20 15:34:51

Wonderful book!!!

nikka · 发表于 2007-2-20 15:36:32

Contents
Preface.... .........................v
1 Introduction to Information Theory . . . . . ........3
2 Probability, Entropy, and Inference . . . . . . ........22
3 More about Inference . . . ..................48
...................... 65
I Data Compression
4 The Source Coding Theorem . . . . . . . . . ........67
5 Symbol Codes .... ... .... ... ... .... ... . 91
6 Stream Codes . .........................110
7 Codes for Integers . . . . . ..................132
.................... 137
IINoisy-ChannelCoding
8 Correlated Random Variables . . . . . . . . . ........138
9 Communication over a Noisy Channel . . . . ........146
10TheNoisy-ChannelCodingTheorem..... ........162
11Error-CorrectingCodesandRealChannels. ........177
............. 191
IIIFurtherTopicsinInformationTheory
12HashCodes:CodesforE?cientInformationRetrieval..193
13BinaryCodes .........................206
14VeryGoodLinearCodesExist........ ........229
15FurtherExercisesonInformationTheory.. ........233
16MessagePassing...... ..................241
17CommunicationoverConstrainedNoiselessChannels...248
18CrosswordsandCodebreaking........ ........260
19WhyhaveSex?InformationAcquisitionandEvolution..269
.................. 281
IVProbabilitiesandInference
20AnExampleInferenceTask:Clustering... ........284
21ExactInferencebyCompleteEnumeration. ........293
22MaximumLikelihoodandClustering..... ........300
23UsefulProbabilityDistributions....... ........311
24ExactMarginalization... ..................319
25ExactMarginalizationinTrellises...... ........324
26ExactMarginalizationinGraphs....... ........334
27Laplace’sMethod..... ..................341

28ModelComparisonandOccam’sRazor... ........343
29MonteCarloMethods... ..................357
30E?cientMonteCarloMethods........ ........387
31IsingModels. .........................400
32ExactMonteCarloSampling......... ........413
33VariationalMethods.... ..................422
34IndependentComponentAnalysisandLatentVariableMod-
elling..... .........................437
35RandomInferenceTopics. ..................445
36DecisionTheory...... ..................451
37BayesianInferenceandSamplingTheory.. ........457
........................ 467
V Neural networks
38IntroductiontoNeuralNetworks....... ........468
39TheSingleNeuronasaClassifier....... ........471
40CapacityofaSingleNeuron ..................483
41LearningasInference... ..................492
42HopfieldNetworks..... ..................505
43BoltzmannMachines.... ..................522
44SupervisedLearninginMultilayerNetworks. ........527
45GaussianProcesses.... ..................535
46Deconvolution .........................549
..................... 555
VISparseGraphCodes
47Low-DensityParity-CheckCodes...... ........557
48ConvolutionalCodesandTurboCodes.... ........574
49Repeat–AccumulateCodes ..................582
50DigitalFountainCodes.. ..................589
.......................... 597
VIIAppendices
A Notation . . . .........................598
B Some Physics . .........................601
C Some Mathematics . . . . . ..................605
Bibliography.... .........................613
Index........ .........................620

Information Theory, Inference, and Learning Algorithms

马上注册，结交更多好友，享用更多功能，让你轻松玩转社区。

Wonderful book!!!

contents include

站长推荐 /2