Information Theory, Inference and Learning Algorithms - D. MacKay (Cambridge, 2003) WW.pdf

文档介绍：InformationTheory,Inference,,Inference,.******@,1996,1997,1998,1999,2000,2001,2002,(aspublished)June26,.-screenontheabovewebsite,inpostscript,djvu,andpdfformats.(.)ContentsPreface................................v1IntroductiontoInformationTheory................32Probability,Entropy,andInference.................223MoreaboutInference........................pression........................654TheSourceCodingTheorem....................675SymbolCodes............................916StreamCodes.............................1107CodesforIntegers..........................132IINoisy-ChannelCoding......................1378CorrelatedRandomVariables....................municationoveraNoisyChannel...............14610TheNoisy-ChannelCodingTheorem................16211Error-CorrectingCodesandRealChannels............177IIIFurtherTopicsinInformationTheory.............19112HashCodes:CodesforE?cientInformationRetrieval.....19313BinaryCodes............................20614VeryGoodLinearCodesExist...................22915FurtherExercisesonInformationTheory.............23316MessagePassing...........................municationoverConstrainedNoiselessChannels......24818CrosswordsandCodebreaking...................26019WhyhaveSex?InformationAcquisitionandEvolution.....269IVProbabilitiesandInference...................28120AnExampleInferenceTask:Clustering..............pleteEnumeration............29322MaximumLikelihoodandClustering................30023UsefulProbabilityDistributions..................31124ExactMarginalization........................31925ExactMarginalizationinTrellises.................32426ExactMarginalizationinGraphs..................33427Laplace’sMethod...........................am’sRazor..............34329Mon