Probability-DependentGain-ScheduledFilteringforStochasticSystemsWithMissingMeasurements

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Probability,Statistics,andRandomProcessesforEngineers(4thEdition).pdf

支持向量机源码，可在www.csie.ntu.edu.tw/~cjlin/libsvm/下载到最新版本，该版本是2013年4月更新的，3.17版。
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以下摘自前述网站：IntroductionLIBSVMisanintegratedsoftwareforsupportvectorclassification,(C-SVC,nu-SVC),regression(epsilon-SVR,nu-SVR)anddistributionestimation(one-classSVM).Itsupportsmulti-classclassification.Sinceversion2.8,itimplementsanSMO-typealgorithmproposedinthispaper:R.-E.Fan,P.-H.Chen,andC.-J.Lin.WorkingsetselectionusingsecondorderinformationfortrainingSVM.JournalofMachineLearningResearch6,1889-1918,2005.Youcanalsofindapseudocodethere.(howtociteLIBSVM)OurgoalistohelpusersfromotherfieldstoeasilyuseSVMasatool.LIBSVMprovidesasimpleinterfacewhereuserscaneasilylinkitwiththeirownprograms.MainfeaturesofLIBSVMincludeDifferentSVMformulationsEfficientmulti-classclassificationCrossvalidationformodelselectionProbabilityestimatesVariouskernels(includingprecomputedkernelmatrix)WeightedSVMforunbalanceddataBothC++andJavasourcesGUIdemonstratingSVMclassificationandregressionPython,R,MATLAB,Perl,Ruby,Weka,CommonLISP,CLISP,Haskell,OCaml,LabVIEW,andPHPinterfaces.C#.NETcodeandCUDAextensionisavailable.It'salsoincludedinsomedataminingenvironments:RapidMiner,PCP,andLIONsolver.Automaticmodelselectionwhichcangeneratecontourofcrossvaliationaccuracy.

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....................

heLeesigmafilterwasdevelopedin1983basedonthesimpleconceptoftwo-sigmaprobability,anditwasrea-sonablyeffectiveinspecklefiltering.However,deficiencieswerediscoveredinproducingbiasedestimationandinblurringanddepressingstrongreflectedtargets.Theadvancementofsyntheticapertureradar(SAR)technologywithhigh-resolutiondataoflargedimensionsdemandsbetterandefficientspecklefilteringalgorithms.Inthispaper,weextendandimprovetheLeesigmafilterbyeliminatingthesedeficiencies

[奥莱理]DoingDataScience(英文版)[奥莱理]DoingDataScienceStraightTalkfromtheFrontline(E-Book)☆图书概要：☆Nowthatpeopleareawarethatdatacanmakethedifferenceinanelectionorabusinessmodel,datascienceasanoccupationisgainingground.Buthowcanyougetstartedworkinginawide-ranging,interdisciplinaryfieldthat’ssocloudedinhype?Thisinsightfulbook,basedonColumbiaUniversity’sIntroductiontoDataScienceclass,tellsyouwhatyouneedtoknow.Inmanyofthesechapter-longlectures,datascientistsfromcompaniessuchasGoogle,Microsoft,andeBaysharenewalgorithms,methods,andmodelsbypresentingcasestudiesandthecodetheyuse.Ifyou’refamiliarwithlinearalgebra,probability,andstatistics,andhaveprogrammingexperience,thisbookisanidealintroductiontodatascience.☆出版信息：☆[作者信息]RachelSchutt,CathyO'Neil[出版机构]奥莱理[出版日期]2013年10月31日[图书页数]406页[图书语言]英语[图书格式]PDF格式

一本从现代角度讲概率的书。
很多的书都推荐看这本书来了解相关知识。
作者前言中自称自己的书与从前的书不同的。

Thisdefinitivetextbookprovidesasolidintroductiontodiscreteandcontinuousstochasticprocesses,tacklingacomplexfieldinawaythatinstilsadeepunderstandingoftherelevantmathematicalprinciples,anddevelopsanintuitivegraspofthewaytheseprinciplescanbeappliedtomodellingreal-worldsystems.ItincludesacarefulreviewofelementaryprobabilityanddetailedcoverageofPoisson,GaussianandMarkovprocesseswithrichlyvariedqueuingapplications.Thetheoryandapplicationsofinference,hypothesistesting,estimation,randomwalks,largedeviations,martingalesandinvestmentsaredeveloped.Writtenbyoneoftheworld'sleadinginformationtheorists,evolvingovertwentyyearsofgraduateclassroomteachingandenrichedbyover300exercises,thisisanexceptionalresourceforanyonelookingtodeveloptheirunderstandingofstochasticprocesses.

StochasticCalculusforFinanceI:TheBinomialAssetPricingModel(SpringerFinance)(Paperback)byStevenE.Shreve(Author)BookDescriptionStochasticCalculusforFinanceevolvedfromthefirsttenyearsoftheCarnegieMellonProfessionalMaster'sprograminComputationalFinance.Thecontentofthisbookhasbeenusedsuccessfullywithstudentswhosemathematicsbackgroundconsistsofcalculusandcalculus-basedprobability.Thetextgivesbothprecisestatementsofresults,plausibilityarguments,andevensomeproofs,butmoreimportantlyintuitiveexplanationsdevelopedandrefinethroughclassroomexperiencewiththismaterialareprovided.Thebookincludesaself-containedtreatmentoftheprobabilitytheoryneededforstochasticcalculus,includingBrownianmotionanditsproperties.Advancedtopicsincludeforeignexchangemodels,forwardmeasures,andjump-diffusionprocesses.Thisbookisbeingpublishedintwovolumes.Thefirstvolumepresentsthebinomialasset-pricingmodelprimarilyasavehicleforintroducinginthesimplesettingtheconceptsneededforthecontinuous-timetheoryinthesecondvolume.Chaptersummariesanddetailedillustrationsareincluded.Classroomtestedexercisesconcludeeverychapter.Someoftheseextendthetheoryandothersaredrawnfrompracticalproblemsinquantitativefinance.AdvancedundergraduatesandMasterslevelstudentsinmathematicalfinanceandfinancialengineeringwillfindthisbookuseful.StevenE.ShreveisCo-FounderoftheCarnegieMellonMSPrograminComputationalFinanceandwinneroftheCarnegieMellonDohertyPrizeforsustainedcontributionstoeducation.Publisher:Springer;1edition(June28,2005)Language:EnglishISBN-10:0387249680ISBN-13:978-0387249681

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。