DaphneKoller关于ProbabilisticGraphicalModels的最权威大作，内容详实深入，是各大名校机器学习和人工智能专业相应课程的指定教材AdaptiveComputationandMachineLearningThomasdietterich,EditorChristopherBishop,DavidHeckerman,MichaelJordan,andMichaelKearns,AssociateEditorsBioinformatics:TheMachinelearningApproach,PierreBaldiandSorenBrunakReinforcementLearning:AnIntroduction,RichardS.SuttonandAndrewG.BartoGraphicalmodelsforMachineLearningandDigitalCommunication,BrendanJ.FreyLearningingraphicalModels,MichaelI.JordanCausation,Prediction,andSearch,2nded,PeterSpirtes,ClarkGlymour,andRichardScheinesPrinciplesofDataMining,DavidHand,HeikkiMannila,andPadhraicSmythBioinformatics:TheMachineLearningApproach,2nded,PierreBaldiandSorenBrunakLearningKernelclassifiers:TheoryandAlgorithms,RalfHerbrichLearningwithKernels:SupportVectorMachines,Regularization,Optimization,andBeyond,BernhardScholkopfandAlexanderJsmolaIntroductiontoMachineLearning,EthemAlpaydinGaussianProcessesforMachineLearning,CarlEdwardRasmussenandChristopherK.I.WilliamsSemi-SupervisedLearning,OlivierChapelle,BernhardScholkopf,andAlexanderZien,edsTheMinimumdescriptionLengthPrinciple,PeterDGrunwaldIntroductiontoStatisticalRelationalLearning,liseGetoorandBenTaskar,edsProbabilisticGraphicalModels:PrinciplesandTechniques,DaphneKollerandNirFriedmanProbabilisticGraphicalModelsPrinciplesandTechniquesDaphnekollerNirfriedmanThemitpressCambridge,MassachusettsLondon,England@2009MassachusettsInstituteofTechnologyAllrightsreserved.Nopartofthisbookmaybereproducedinanyformbyanyelectronicormechanicalmeans(includingphotocopying,recording,orinformationstorageandretrieval)withoutpermissioninwritingfromthepublisherForinformationaboutspecialquantitydiscounts,pleaseemailspecial_sales@mitpress.mit.eduThisbookwassetbytheauthorsinBlFX2EPrintedandboundintheunitedstatesofamericaLibraryofCongressCataloging-in-PublicationDataKoller,DaphneProbabilisticGraphicalModels:PrinciplesandTechniquesDaphneKollerandNirFriedmanpcm.-(Adaptivecomputationandmachinelearning)IncludesbibliographicalreferencesandindexisBn978-0-262-01319-2(hardcover:alk.paper1.Graphicalmodeling(Statistics)2.Bayesianstatisticaldecisiontheory--Graphicmethods.IKoller,Daphne.II.Friedman,NirQA279.5.K652010519.5’420285-dc222009008615109876543ToourfamiliesmyparentsDovandditzamyhusbanddanmydaughtersnatalieandmayaDKmyparentsNogaandGadmywifemychildrenroyandliorMEAsfarasthelawsofmathematicsrefertoreality,theyarenotcertain,asfarastheyarecertain,theydonotrefertorealityAlberteinstein1956Whenwetrytopickoutanythingbyitself,wefindthatitisboundfastbyathousandinvisiblecordsthatcannotbebroken,toeverythingintheuniverseJohnMuir,1869Theactualscienceoflogicisconversantatpresentonlywiththingseithercertain,impossible,orentirelydoubtful.Thereforethetruelogicforthisworldisthecalculusofprobabilities,whichtakesaccountofthemagnitudeoftheprobabilitywhichis,oroughttobe,inareasonableman'smindJamesClerkMaxwell,1850Thetheoryofprobabilitiesisatbottomnothingbutcommonsensereducedtocalculus;itenablesustoappreciatewithexactnessthatwhichaccuratemindsfeelwithasortofinstinctforwhichofttimestheyareunabletoaccount.PierreSimonLaplace,1819MisunderstandingofprobabilitymaybethegreatestofallimpedimentstoscientificliteracyStephenJayGouldContentsAcknowledgmentsListoffiguresListofalgorithmsListofboxesXXX1IntroductionL1Motivation11.2StructuredProbabilisticModels21.2.1ProbabilisticGraphicalModels31.2.2Representation,Inference,Learning51.3Overviewandroadmap61.3.1OverviewofChapters61.3.2Readersguide1.3.3ConnectiontoOtherDisciplines1.4Historicalnotes122Foundations2.1ProbabilityTheory2.1.1ProbabilityDistributions152.1.2BasicConceptsinProbability182.1.3RandomVariablesandJointDistributions192.1.4IndependenceandConditionalIndependence2:2.1.5QueryingaDistribution2.1.6ContinuousSpaces272.1.7ExpectationandVariance312.2Graphs342.2.1Nodesandedges342.2.2Subgraphs352.2.3Pathsandtrails36

PDF版，HenryStarkJohnW.Woods第四版Probability,Statistics,andRandomProcessesforEngineers习题解答。

使用redmine缺陷管理工具的测试人员注意了，本代码是实现统计redmine的数据（本阶段、本版本、严重等级、指派人员、结构分布情况），并实现自动化发送阶段性测试报告邮件脚本，也有现成的exe程序，大家可是试试（exe是使用内网地址，大家可能会运行失败，这也是保证公司机密不被泄露），如果大家有问题，可以随时私聊我

摘要:本文介绍了如何将用户自定义的功能模块与IBMSPSSStatistics进行集成，如何利用Statistics提供的统计分析方法对功能模块的输入数据进行预处理，并对集成结果进行分析与演示。
近年来，商业分析（BusinessAnalytics，BA）软件逐渐成为企业增强洞察力的利器。
其中，IBMSPSSStatistics是统计分析领域中久享盛名的应用软件。
企业在实际运营中，已根据不同的业务需求，开发或购买了满足自身需求的商业数据整合方案，并期待与Statistics进行集成，以便更高效、准确的分析数据，提取数据中隐含的信息。
Statistics不仅为用户提供了丰富的统计算法来帮助用户分析

Statisticalmethodsareakeypartofofdatascience,yetveryfewdatascientistshaveanyformalstatisticstraining.Coursesandbooksonbasicstatisticsrarelycoverthetopicfromadatascienceperspective.Thispracticalguideexplainshowtoapplyvariousstatisticalmethodstodatascience,tellsyouhowtoavoidtheirmisuse,andgivesyouadviceonwhat'simportantandwhat'snot.Manydatascienceresourcesincorporatestatisticalmethodsbutlackadeeperstatisticalperspective.Ifyou'refamiliarwiththeRprogramminglanguage,andhavesomeexposuretostatistics,thisquickreferencebridgesthegapinanaccessible,readableformat.Whyexploratorydataanalysisisakeypreliminarystepindatascience;Howrandomsamplingcanreducebiasandyieldahigherqualitydataset,evenwithbigdata;Howtheprinciplesofexperimentaldesignyielddefinitiveanswerstoquestions;Howtouseregressiontoestimateoutcomesanddetectanomalies;Keyclassificationtechniquesforpredictingwhichcategoriesarecordbelongsto;Statisticalmachinelearningmethodsthat"learn"fromdata;Unsupervisedlearningmethodsforextractingmeaningfromunlabeleddata.

DetailsTitle:DigitalImageProcessing,4thEditionAuthor:RafaelC.Gonzalez,RichardE.WoodsLength:1192pagesEdition:4Language:EnglishPublisher:PearsonPublicationDate:2017-03-30ISBN-10:0133356728ISBN-13:9780133356724SalesRank:#680902(SeeTop100Books)CategoriesComputers&TechnologyEngineering&TransportationEngineeringTextbooksDescriptionIntroduceyourstudentstoimageprocessingwiththeindustry’smostprizedtextFor40years,ImageProcessinghasbeenthefoundationaltextforthestudyofdigitalimageprocessing.Thebookissuitedforstudentsatthecollegeseniorandfirst-yeargraduatelevelwithpriorbackgroundinmathematicalanalysis,vectors,matrices,probability,statistics,linearsystems,andcomputerprogramming.Asinallearliereditions,thefocusofthiseditionofthebookisonfundamentals.The4thEdition,whichcelebratesthebook’s40thanniversary,isbasedonanextensivesurveyoffaculty,students,andindependentreadersin150institutionsfrom30countries.Theirfeedbackledtoexpandedornewcoverageoftopicssuchasdeeplearninganddeepneuralnetworks,includingconvolutionalneuralnets,thescale-invariantfeaturetransform(SIFT),maximally-stableextremalregions(MSERs),graphcuts,k-meansclusteringandsuperpixels,activecontours(snakesandlevelsets),andexacthistogrammatching.?Majorimprovementsweremadeinreorganizingthematerialonimagetransformsintoamorecohesivepresentation,andinthediscussionofspatialkernelsandspatialfiltering.?Majorrevisionsandadditionsweremadetoexamplesandhomeworkexercisesthroughoutthebook.Forthefirsttime,weaddedMATLABprojectsattheendofeverychapter,andcompiledsupportpackagesforyouandyourteachercontaining,solutions,imagedatabases,andsamplecode.Thesupportmaterialsforthistitlecanbefoundatwww.ImageProcessingPlace.com

PrefaceIwrotethisbooktohelpmachinelearningpractitioners,likeyou,getontopoflinearalgebra,fast.LinearAlgebraIsImportantinMachineLearningThereisnodoubtthatlinearalgebraisimportantinmachinelearning.Linearalgebraisthemathematicsofdata.It’sallvectorsandmatricesofnumbers.Modernstatisticsisdescribedusingthenotationoflinearalgebraandmodernstatisticalmethodsharnessthetoolsoflinearalgebra.Modernmachinelearningmethodsaredescribedthesameway,usingthenotationsandtoolsdrawndirectlyfromlinearalgebra.Evensomeclassicalmethodsusedinthefield,suchaslinearregressionvialinearleastsquaresandsingular-valuedecomposition,arelinearalgebramethods,andothermethods,suchasprincipalcomponentanalysis,werebornfromthemarriageoflinearalgebraandstatistics.Toreadandunderstandmachinelearning,youmustbeabletoreadandunderstandlinearalgebra.PractitionersStudyLinearAlgebraTooEarlyIfyouaskhowtogetstartedinmachinelearning,youwillverylikelybetoldtostartwithlinearalgebra.Weknowthatknowledgeoflinearalgebraiscriticallyimportant,butitdoesnothavetobetheplacetostart.Learninglinearalgebrafirst,thencalculus,probability,statistics,andeventuallymachinelearningtheoryisalongandslowbottom-uppath.Abetterfitfordevelopersistostartwithsystematicproceduresthatgetresults,andworkbacktothedeeperunderstandingoftheory,usingworkingresultsasacontext.Icallthisthetop-downorresults-firstapproachtomachinelearning,andlinearalgebraisnotthefirststep,butperhapsthesecondorthird.PractitionersStudyTooMuchLinearAlgebraWhenpractitionersdocirclebacktostudylinearalgebra,theylearnfarmoreofthefieldthanisrequiredfororrelevanttomachinelearning.Linearalgebraisalargefieldofstudythathastendrilsintoengineering,physicsandquantumphysics.Therearealso

经典教材的详细答案，让你学习不在苦恼，不在彷徨，让你赢在起跑线上。

Probability,Statistics,andRandomProcessesforEngineers(4thEdition).pdf

IntroductiontoMathematicalStatistics(7thEdition)byRobertV.Hogg,JoesephMcKean,AllenTCraighttp://www.amazon.com/Introduction-Mathematical-Statistics-7th-Edition/dp/0321795431

在日常工作中，钉钉打卡成了我生活中不可或缺的一部分。然而，有时候这个看似简单的任务却给我带来了不少烦恼。 每天早晚，我总是得牢记打开钉钉应用，点击"工作台"，再找到"考勤打卡"进行签到。有时候因为工作忙碌，会忘记打卡，导致考勤异常，影响当月的工作评价。而且，由于我使用的是苹果手机，有时候系统更新后，钉钉的某些功能会出现异常，使得打卡变得更加麻烦。 另外，我的家人使用的是安卓手机，他们也经常抱怨钉钉打卡的繁琐。尤其是对于那些不太熟悉手机操作的长辈来说，每次打卡都是一次挑战。他们总是担心自己会操作失误，导致打卡失败。 为了解决这些烦恼，我开始思考是否可以通过编写一个全自动化脚本来实现钉钉打卡。经过一段时间的摸索和学习，我终于成功编写出了一个适用于苹果和安卓系统的钉钉打卡脚本。