概率风险分析

定　价：¥78.00

作　者：	Tim Bedford，Roger Cooke著
出版社：	世界图书出版公司北京公司
丛编项：
标　签：	暂缺

购买这本书可以去

ISBN：	9787506259453	出版时间：	2003-09-01	包装：	平装
开本：	23cm	页数：	393	字数：

内容简介

　　Probabilistic risk analysis differs from other areas of applied science because it attempts to model events that （almost） never occur. When such an event does occur then the underlying systems and organizations are often changed so that the event cannot occur in the same way again. Because of this， the probabilistic risk analyst must have a strong conceptual and mathematical background. The first chapter surveys the history of risk analysis applications. Chapter 2 explains why probability is used to model uncertainty and why we adopt a subjective definition of probability in spite of its limitations. Chapters 3 and 4 provide the technical background in probability and statistics that is used in the rest of the book. The remaining chapters are more-or-less technically independent of each other， except that Chapter 7 must follow Chapter 6， and 14 should follow 13. The final chapter gives a broad overview of risk measurement problems and looks into the future of risk analysis.此书为英文版！

作者简介

暂缺《概率风险分析》作者简介

图书目录

Illustrations
Tables
Preface
PartI:Introduction
1Probabilisticriskanalysis
1.1Historicaloverview
1.1.1Theaerospacesector
1.1.2Thenuclearsector
1.1.3Thechemicalprocesssector
1.1.4Thelessrecentpast
1.2Whatisthedefinitionofrisk?
1.3Scopeofprobabilisticriskanalyses
1.4Riskanalysisresources
1.4.1Importantjournals
1.4.2Handbooks
1.4.3Professionalorganizations
1.4.4Internet
PartII:Theoreticalissuesandbackground
2Whatisuncertainty?
2.1Themeaningofmeaning
2.2Themeaningofuncertainty
2.3Probabilityaxioms
2.3.1Interpretations
2.4Savage'stheoryofrationaldecision
2.4.1Savage'saxioms
2.4.2Quantitativeprobability
2.4.3Utility
2.4.4Observation
2.5Measurementofsubjectiveprobabilities
2.6Differenttypesofuncertainty
2.7Uncertaintyaboutprobabilities
3Probabilistiemethods
3.1Reviewofelementaryprobabilitytheory
3.2Randomvariables
3.2.1Moments
3.2.2Severalrandomvariables
3.2.3Correlations
3.2.4Failurerates
3.3Theexponentiallifedistribution
3.3.1Constanttestintervals
3.3.2Exponentialfailureandrepair
3.4ThePoissondistribution
3.5Thegammadistribution
3.6Thebetadistribution
3.7Thelognormaldistribution
3.8Stochasticprocesses
3.9Approximatingdistributions
4Statisticalinference
4.1Foundations
4.2Bayesianinference
4.2.1Bayes'Theorem
4.2.2Anexamplewiththeexponentialdistribution
4.2.3Conjugatedistributions
4.2.4Firstfindyourprior
4.2.5Pointestimatorsfromtheparameterdistribution
4.2.6Asymptoticbehaviouroftheposterior
4.3Classicalstatisticalinference
4.3.1Estimationofparameters
4.3.2Non-parametricestimation
4.3.3Confidenceintervals
4.3.4Hypothesistesting
5WeibullAnalysis
5.1Definitions
5.2Graphicalmethodsforparameterfitting
5.2.1Rankordermethods
5.2.2Suspendedorcensoreditems
5.2.3TheKaplan-Meierestimator
5.3Maximumlikelihoodmethodsforparameterestimation
5.4Bayesianestimation
5.5Extremevaluetheory
PartIII:Systemanalysisandquantification
6Faultandeventtrees
6.1Faultandeventtrees.
6.2Theaimofafault-treeanalysis
6.3Thedefinitionofasystemandofatopevent
6.3.1Externalboundaries
6.3.2Internalboundaries
6.3.3Temporalboundaries
6.4Whatclassesoffaultscanoccur?
6.4.1Activeandpassivecomponents
6.4.2Primary,secondaryandcommandfaults
6.4.3Failuremodes,effectsandmechanisms
6.5Symbolsforfaulttrees
6.6Faulttreeconstruction
6.7Examples
6.7.1Reactorvessel
6.7.2NewWaterwaybarrier
6.8Minimalpathandcutsetsforcoherentsystems
6.8.1Cutsets
6.8.2Pathsets
6.9Settheoreticdescriptionofcutandpathsets
6.9.1Booleanalgebra
6.9.2Cutsetrepresentation
6.9.3Pathsetrepresentation
6.9.4Minimalcutset/pathsetduality
6.9.5Parallelandseriessystems
6.10Estimatingtheprobabilityofthetopevent
6.10.1Commoncause
7Faulttrees-analysis
7.1TheMOCUSalgorithmforfindingminimalcutsets
7.1.1Topdownsubstitution
7.1.2Bottomupsubstitution
7.1.3Treepruning
7.2Binarydecisiondiagramsandnewalgorithms
7.2.1Primeimplicantscalculation
7.2.2Minimalp-cuts
7.2.3Probabilitycalculations
7.2.4Examples
7.2.5ThesizeoftheBDD
7.3Importance
8Dependentfailures
8.1Introduction
8.2Componentfailuredataversusincidentreporting
8.3Preliminaryanalysis
8.4Inter-systemdependencies
8.5Inter-componentdependencies-commoncausefailure
8.6Thesquarerootboundingmodel
8.7TheMarshall-Olkinmodel
8.8Thebeta-factormodel
8.8.1Parameterestimation
8.9Thebinomialfailureratemodel
8.10The-factormodel
8.11Othermodels
9Reliabilitydatabases
9.1Introduction
9.2Maintenanceandfailuretaxonomies
9.2.1Maintenancetaxonomy
9.2.2Failuretaxonomy
9.2.3Operatingmodes;failurecauses;failuremechanisms
andfailuremodes
9.3Datastructure
9.3.1Operationsondata
9.4Dataanalysiswithoutcompetingrisks
9.4.1Demandrelatedfailures:non-degradablecomponents
9.4.2Demandrelatedfailures:degradablecomponents
9.4.3Timerelatedfailures;nocompetingrisks
9.5Competingriskconceptsandmethods
9.5.1Subsurvivorfunctionsandidentifiability
9.5.2ColoredPoissonrepresentationofcompetingrisks
9.6Competingriskmodels
9.6.1Independentexponentialcompetingrisk
9.6.2Randomclipping
9.6.3Randomsigns
9.6.4Conditionallyindependentcompetingrisks
9.6.5Timewindowcensoring
9.7Uncertainty
9.7.1Uncertaintyduetonon-identifiability:boundsinthe
absenceofsamplingfluctuations
9.7.2Accountingforsamplingfluctuations
9.7.3SamplingfluctuationsofPetersonbounds
9.8Examplesofdependentcompetingriskmodels
9.8.1Failureeffect
9.8.2Actiontaken
9.8.3Methodofdetection
9.8.4Subcomponent
9.8.5Conclusions
10Expertopinion
10.1Introduction
10.2Genericissuesintheuseofexpertopinion
10.3Bayesiancombinationsofexpertassessments
10.4Non-Bayesiancombinationsofexpertdistributions
10.5Linearopinionpools
10.6Performancebasedweighting-theclassicalmodel
10.6.1Calibration
10.6.2Information
10.6.3Determiningtheweights
10.6.4Approximationofexpertdistributions
10.7Casestudy-uncertaintyindispersionmodeling
11Humanreliability
11.1Introduction
11.2Genericaspectsofahumanreliabilityanalysis
11.2.1Humanerrorprobabilities
11.2.2Taskanalysis
11.2.3Performanceanderrortaxonomy
11.2.4Performanceshapingfactors
11.3THERP-techniqueforhumanerrorrateprediction
11.3.1Humanerroreventtrees
11.3.2Performanceshapingfactors
11.3.3Dependence
11.3.4Timedependenceandrecovery
11.3.5DistributionsforHEPs
11.4TheSuccessLikelihoodIndexMethodology
11.5Timereliabilitycorrelations
11.6AbsoluteProbabilityJudgement
11.7Influencediagrams
11.8Conclusions
12Softwarereliability
12.1Qualitativeassessment-waystofinderrors
12.1.1FMECAsofsoftware-basedsystems
12.1.2Formaldesignandanalysismethods
12.1.3Softwaresneakanalysis
12.1.4Softwaretesting
12.1.5Errorreporting
12.2Softwarequalityassurance
12.2.1Softwaresafetylife-cycles
12.2.2Developmentphasesandreliabilitytechniques
12.2.3Softwarequality
12.2.4Softwarequalitycharacteristics
12.2.5Softwarequalitymetrics
12.3Softwarereliabilityprediction
12.3.1Errorseeding
12.3.2TheJelinski-Morandamodel
12.3.3Littlewood'smodel
12.3.4TheLittlewood-Verralmodel
12.3.5TheGoel-Okumotomodel
12.4Calibrationandweighting
12.4.1Calibration
12.4.2Weightedmixturesofpredictors
12.5Integrationerrors
12.6Example
PartIV:Uncertaintymodelingandriskmeasurement
13Decisiontheory
13.1Preferencesoveractions
13.2Decisiontreeexample
13.3Thevalueofinformation
13.3.1Whendoobservationshelp?
13.4Utility
13.5Multi-attributedecisiontheoryandvaluemodels
13.5.1Attributehierarchies
13.5.2Theweightingfactorsmodel
13.5.3Mutualpreferentialindependence
13.5.4Conditionalpreferentialindependence
13.5.5Multi-attributeutilitytheory
13.5.6Whendowemodeltheriskattitude?
13.5.7Trade-offsthroughtime
13.6Otherpopularmodela
13.6.1Cost-benefitanalysis
13.6.2Theanalytichierarchyprocess
13.7Conclusions
14Influencediagramsandbeliefnets
14.1Beliefnetworks
14.2Conditionalindependence
14.3Directedacyclicgraphs
14.4Constructionofinfluencediagrams
14.4.1Modelverification
14.5Operationsoninfluencediagrams
14.5.1Arrowreversal
14.5.2Chancenoderemoval
14.6Evaluationofinfluencediagrams
14.7Therelationwithdecisiontrees
14.8AnexampleofaBayesiannetapplication
15Projectriskmanagement
15.1Riskmanagementmethods
15.1.1Identificationofuncertainties
15.1.2Quantificationofuncertainties
15.1.3Calculationofprojectrisk
15.2TheCriticalPathMethod(CPM)
15.3Expertjudgementforquantifyinguncertainties
15.4Buildingincorrelations
15.5Simulationofcompletiontimes
15.6Valueofmoney
15.7Casestudy
16Probabilisticinversiontechniquesforuncertaintyanalysis
16.1Elicitationvariablesandtargetvariables
16.2Mathematicalformulationofprobabilisticinversion
16.3PREJUDICE
16.3.1Heuristics
16.3.2Solvingforminimuminformation
16.4InfeasibilityproblemsandPARFUM
16.5Example
17Uncertaintyanalysis
17.1Introduction
17.1.1Mathematicalformulationofuncertaintyanalysis
17.2MonteCarlosimulation
17.2.1Univariatedistributions
17.2.2Multivariatedistributions
17.2.3Transformsofjointnormals
17.2.4Rankcorrelationtrees
17.2.5Vines
17.3Examples:uncertaintyanalysisforsystemfailure
17.3.1Thereactorexample
17.3.2Seriesandparallelsystems
17.3.3Dispersionmodel
17.5Appendix:bivariateminimallyinformativedistributions
17.5.1Minimalinformationdistributions
18Riskmeasurementandregulation
18.1Singlestatisticsrepresentingrisk
18.1.1Deathspermillion
18.1.2Lossoflifeexpectancy
18.1.3Deltayearlyprobabilityofdeath
18.1.4Activityspecifichourlymortalityrate
18.1.5Deathperunitactivity
18.2Frequencyvsconsequencelines
18.2.1Groupriskcomparisons;ccdfmethod
18.2.2Totalrisk
18.2.3Expecteddisutility
18.2.4UncertaintyaboutthefCcurve
18.2.5Benefits
18.3Riskregulation
18.3.1ALARP
18.3.2Thevalueofhumanlife
18.3.3Limitsofriskregulation
18.4Perceivingandacceptingrisks
18.4.1Riskperception
18.4.2Acceptabilityofrisks
18.5Beyondriskregulation:compensation,tradingandethics
Bibliography
Index