CONTENTPART1BASICSOFINFERENCEOVERNETWORKSCHAPTER1AsynchronousAdaptiveNetworksCHAPTER2EstimationandDetectionOverAdaptiveNetworksCHAPTER3MultitaskLearningOverAdaptiveNetworksWithGroupingCHAPTER4BayesianApproachtoCollaborativeInferenceinNetworksCHAPTER5MultiagentDistributedOptimizationCHAPTER6DistributedKalmanandParticleFilteringCHAPTER7GameTheoreticLearningPART2SIGNALPROCESSINGONGRAPHSCHAPTER8GraphSignalProcessing.CHAPTER9SamplingandRecoveryofGraphSignalsCHAPTER10BayesianActivelearningonGraphs.CHAPTER11DesignofGraphFiltersandFilterbanksCHAPTER12StatisticalGraphSignalProcessing:StationarityandSpectralEstimationCHAPTER13InferenceofGraphTopologyCHAPTER14PartiallyAbsorbingRanclomWalks:AUnifieclFrameworkforLearningonGraphsPART3DISTRIBUTEDCOMMUNICATIONS,NETWORKING,ANDSENSING.....

ContentsPrefacevTypographicalConventionsxi1Introduction11.1AQuickOverviewofS.......................31.2UsingS...............................51.3AnIntroductorySession......................61.4WhatNext?.............................122DataManipulation132.1Objects...............................132.2Connections.............................202.3DataManipulation.........................272.4TablesandCross-Classification...................373TheSLanguage413.1LanguageLayout..........................413.2MoreonSObjects.........................443.3ArithmeticalExpressions......................473.4CharacterVectorOperations....................513.5FormattingandPrinting.......................543.6CallingConventionsforFunctions.................553.7ModelFormulae...........................563.8ControlStructures..........................583.9ArrayandMatrixOperations....................603.10IntroductiontoClassesandMethods................664Graphics694.1GraphicsDevices..........................714.2BasicPlottingFunctions......................72viiviiiContents4.3EnhancingPlots...........................774.4FineControlofGraphics......................824.5TrellisGraphics...........................895UnivariateStatistics1075.1ProbabilityDistributions......................1075.2GeneratingRandomData......................1105.3DataSummaries...........................1115.4ClassicalUnivariateStatistics....................1155.5RobustSummaries.........................1195.6DensityEstimation.........................1265.7BootstrapandPermutationMethods................1336LinearStatisticalModels1396.1AnAnalysisofCovarianceExample................1396.2ModelFormulaeandModelMatrices...............1446.3RegressionDiagnostics.......................1516.4SafePrediction....................

IllustratesarangeofstatisticalcomputationsinRusingtheRcpppackageProvidesageneralintroductiontoextendingRwithC++codeFeaturesanappendixforRusersnewtotheC++programminglanguageRcpppackagesarepresentedinthecontextofusefulapplicationcasestudies

ExploratoryDataAnalysisUsingRprovidesaclassroom-testedintroductiontoexploratorydataanalysis(EDA)andintroducestherangeof"interesting"–good,bad,andugly–featuresthatcanbefoundindata,andwhyitisimportanttofindthem.ItalsointroducesthemechanicsofusingRtoexploreandexplaindata.Thebookbeginswithadetailedoverviewofdata,exploratoryanalysis,andR,aswellasgraphicsinR.Itthenexploresworkingwithexternaldata,linearregressionmodels,andcraftingdatastories.ThesecondpartofthebookfocusesondevelopingRprograms,includinggoodprogrammingpracticesandexamples,workingwithtextdata,andgeneralpredictivemodels.Thebookendswithachapteron"keepingitalltogether"thatincludesmanagingtheRinstallation,managingfiles,documenting,andanintroductiontoreproduciblecomputing.Thebookisdesignedforbothadvancedundergraduate,entry-levelgraduatestudents,andworkingprofessionalswithlittletonopriorexposuretodataanalysis,modeling,statistics,orprogramming.itkeepsthetreatmentrelativelynon-mathematical,eventhoughdataanalysisisaninherentlymathematicalsubject.Exercisesareincludedattheendofmostchapters,andaninstructor'ssolutionmanualisavailable.AbouttheAuthor:RonaldK.PearsonholdsthepositionofSeniorDataScientistwithGeoVera,apropertyinsurancecompanyinFairfield,California,andhehaspreviouslyheldsimilarpositionsinavarietyofapplicationareas,includingsoftwaredevelopment,drugsafetydataanalysis,andtheanalysisofindustrialprocessdata.HeholdsaPhDinElectricalEngineeringandComputerSciencefromtheMassachusettsInstituteofTechnologyandhaspublishedconferenceandjournalpapersontopicsrangingfromnonlineardynamicmodelstructureselectiontotheproblemsofdisguisedmissingdatainpredictivemodeling.Dr.Pearsonhasauthoredorco-authoredbooksincludingExploringDatainEngineeri

Theavailabilityoflargedatasetshasallowedresearcherstouncovercomplexpropertiessuchaslarge-scalefluctuationsandheterogeneitiesinmanynetworks,leadingtothebreakdownofstandardtheoreticalframeworksandmodels.Untilrecentlythesesystemswereconsideredashaphazardsetsofpointsandconnections.Recentadvanceshavegeneratedavigorousresearcheffortinunderstandingtheeffectofcomplexconnectivitypatternsondynamicalphenomena.Thisbookpresentsacomprehensiveaccountoftheseeffects.Avastnumberofsystems,fromthebraintoecosystems,powergridsandtheInternet,canberepresentedaslargecomplexnetworks.Thisbookwillinterestgraduatestudentsandresearchersinmanydisciplines,fromphysicsandstatisticalmechanics,tomathematicalbiologyandinformationscience.Itsmodularapproachallowsreaderstoreadilyaccessthesectionsofmostinteresttothem,andcomplicatedmathsisavoidedsothetextcanbeeasilyfollowedbynon-expertsinthesubject.

GapStatistic算法研究，详细的代码以及分析过程。

作者MoodA.M.,GraybillF.A.,BoesD.C.书名Introductiontothetheoryofstatistics(3rded.,McGraw-Hill,1974)统计理论介绍第三版方便大家学习

Multivariatetimeseriesanalysisconsiderssimultaneouslymultipletimeseries.Itisabranchofmultivariatestatisticalanalysisbutdealsspecificallywithdependentdata.Itis,ingeneral,muchmorecomplicatedthantheunivariatetimeseriesanalysis,especiallywhenthenumberofseriesconsideredislarge.Westudythismorecomplicatedstatisticalanalysisinthisbookbecauseinreallifedecisionsofteninvolvemultipleinter-relatedfactorsorvariables.Understandingtherelationshipsbetweenthosefactorsandprovidingaccuratepredictionsofthosevariablesarevaluableindecisionmaking.Theobjectivesofmultivariatetimeseriesanalysisthusinclude1.Tostudythedynamicrelationshipsbetweenvariables2.Toimprovetheaccuracyofprediction

SASProgramminginthePharmaceuticalIndustry,SecondEditionbyJackShostakEnglish|2014|ISBN:1612906044|308pages|PDF|14MBThiscomprehensiveresourceprovideson-the-jobtrainingforstatisticalprogrammerswhouseSASinthepharmaceuticalindustry.Thisone-stopresourceoffersacompletereviewofwhatentry-tointermediate-levelstatisticalprogrammersneedtoknowinordertohelpwiththeanalysisandreportingofclinicaltrialdatainthepharmaceuticalindustry.SASProgramminginthePharmaceuticalIndustry,SecondEditionbeginswithanintroductiontothepharmaceuticalindustryandtheworkenvironmentofastatisticalprogrammer.Thenitgivesachronologicalexplanationofwhatyouneedtoknowtodothejob.Itincludesinformationonimportingandmassagingdataintoanalysisdatasets,producingclinicaltrialoutput,andexportingdata.ThiseditionhasbeenupdatedforSAS9.4,anditfeaturesnewgraphicsaswellasallnewexamplesusingCDISCSDTMorADaMmodeldatastructures.Whetheryou'reanoviceseekinganintroductiontoSASprogramminginthepharmaceuticalindustryorajunior-levelprogrammerexploringnewapproachestoproblemsolving,thisreal-worldreferenceguideoffersawealthofpracticalsuggestionstohelpyousharpenyourskills.

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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。