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LEADER 00000cam a2200961 i 4500
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082 00 519.2
082 00 519.2|223
099 eBook O'Reilly for Public Libraries
245 00 Introduction to imprecise probabilities /|cedited by
Thomas Augustin, Department of Statistics, LMU Munich,
Germany, Frank P.A. Coolen, Department of Mathematical
Sciences, Durham University, UK, Gert de Cooman, SYSTeMS
Research Group, Ghent University, Belgium, Matthias C.M.
Troffaes, Department of Mathematical Sciences, Durham
University, UK.|h[O'Reilly electronic resource]
264 1 Chichester, West Sussex :|bJohn Wiley & Sons Inc.,|c2014.
300 1 online resource (xxvi, 404 pages)
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
504 Includes bibliographical references and index.
505 00 |6880-01|gMachine generated contents note:|g1.1.
|tIntroduction /|rErik Quaeghebeur --|g1.2.|tReasoning
about and with sets of desirable gambles /|rErik
Quaeghebeur --|g1.2.1.|tRationality criteria /|rErik
Quaeghebeur --|g1.2.2.|tAssessments avoiding partial or
sure loss /|rErik Quaeghebeur --|g1.2.3.|tCoherent sets of
desirable gambles /|rErik Quaeghebeur --|g1.2.4.|tNatural
extension /|rErik Quaeghebeur --|g1.2.5.|tDesirability
relative to subspaces with arbitrary vector orderings /
|rErik Quaeghebeur --|g1.3.|tDeriving and combining sets
of desirable gambles /|rErik Quaeghebeur --|g1.3.1.
|tGamble space transformations /|rErik Quaeghebeur --
|g1.3.2.|tDerived coherent sets of desirable gambles /
|rErik Quaeghebeur --|g1.3.3.|tConditional sets of
desirable gambles /|rErik Quaeghebeur --|g1.3.4.|tMarginal
sets of desirable gambles /|rErik Quaeghebeur --|g1.3.5.
|tCombining sets of desirable gambles /|rErik Quaeghebeur
--|g1.4.|tPartial preference orders /|rErik Quaeghebeur --
|g1.4.1.|tStrict preference /|rErik Quaeghebeur --|g1.4.2.
|tNonstrict preference /|rErik Quaeghebeur --|g1.4.3.
|tNonstrict preferences implied by strict ones /|rErik
Quaeghebeur --|g1.4.4.|tStrict preferences implied by
nonstrict ones /|rErik Quaeghebeur --|g1.5.|tMaximally
committal sets of strictly desirable gambles /|rErik
Quaeghebeur --|g1.6.|tRelationships with other,
nonequivalent models /|rErik Quaeghebeur --|g1.6.1.
|tLinear previsions /|rErik Quaeghebeur --|g1.6.2.|tCredal
sets /|rErik Quaeghebeur --|g1.6.3.|tTo lower and upper
previsions /|rErik Quaeghebeur --|g1.6.4.|tSimplified
variants of desirability /|rErik Quaeghebeur --|g1.6.5.
|tFrom lower previsions /|rErik Quaeghebeur --|g1.6.6.
|tConditional lower previsions /|rErik Quaeghebeur --
|g1.7.|tFurther reading /|rErik Quaeghebeur --
|tAcknowledgements /|rErik Quaeghebeur --|g2.1.
|tIntroduction /|rEnrique Miranda /|rGert de Cooman --
|g2.2.|tCoherent lower previsions /|rEnrique Miranda /
|rGert de Cooman --|g2.2.1.|tAvoiding sure loss and
coherence /|rGert de Cooman /|rEnrique Miranda --|g2.2.2.
|tLinear previsions /|rEnrique Miranda /|rGert de Cooman -
-|g2.2.3.|tSets of desirable gambles /|rGert de Cooman /
|rEnrique Miranda --|g2.2.4.|tNatural extension /|rEnrique
Miranda /|rGert de Cooman --|g2.3.|tConditional lower
previsions /|rGert de Cooman /|rEnrique Miranda --|g2.3.1.
|tCoherence of a finite number of conditional lower
previsions /|rEnrique Miranda /|rGert de Cooman --|g2.3.2.
|tNatural extension of conditional lower previsions /
|rGert de Cooman /|rEnrique Miranda --|g2.3.3.|tCoherence
of an unconditional and a conditional lower prevision /
|rEnrique Miranda /|rGert de Cooman --|g2.3.4.|tUpdating
with the regular extension /|rGert de Cooman /|rEnrique
Miranda --|g2.4.|tFurther reading /|rGert de Cooman /
|rEnrique Miranda --|g2.4.1.|twork of Williams /|rGert de
Cooman /|rEnrique Miranda --|g2.4.2.|twork of Kuznetsov /
|rEnrique Miranda /|rGert de Cooman --|g2.4.3.|twork of
Weichselberger /|rEnrique Miranda /|rGert de Cooman --
|tAcknowledgements /|rGert de Cooman /|rEnrique Miranda --
|g3.1.|tIntroduction /|rGert de Cooman /|rEnrique Miranda
--|g3.2.|tIrrelevance and independence /|rGert de Cooman /
|rEnrique Miranda --|g3.2.1.|tEpistemic irrelevance /
|rGert de Cooman /|rEnrique Miranda --|g3.2.2.|tEpistemic
independence /|rGert de Cooman /|rEnrique Miranda --
|g3.2.3.|tEnvelopes of independent precise models /|rGert
de Cooman /|rEnrique Miranda --|g3.2.4.|tStrong
independence /|rGert de Cooman /|rEnrique Miranda --
|g3.2.5.|tformalist approach to independence /|rGert de
Cooman /|rEnrique Miranda --|g3.3.|tInvariance /|rGert de
Cooman /|rEnrique Miranda --|g3.3.1.|tWeak invariance /
|rGert de Cooman /|rEnrique Miranda --|g3.3.2.|tStrong
invariance /|rGert de Cooman /|rEnrique Miranda --|g3.4.
|tExchangeability /|rGert de Cooman /|rEnrique Miranda --
|g3.4.1.|tRepresentation theorem for finite sequences /
|rGert de Cooman /|rEnrique Miranda --|g3.4.2.
|tExchangeable natural extension /|rGert de Cooman /
|rEnrique Miranda --|g3.4.3.|tExchangeable sequences /
|rGert de Cooman /|rEnrique Miranda --|g3.5.|tFurther
reading /|rGert de Cooman /|rEnrique Miranda --|g3.5.1.
|tIndependence /|rGert de Cooman /|rEnrique Miranda --
|g3.5.2.|tInvariance /|rGert de Cooman /|rEnrique Miranda
--|g3.5.3.|tExchangeability /|rGert de Cooman /|rEnrique
Miranda --|tAcknowledgements /|rGert de Cooman /|rEnrique
Miranda --|g4.1.|tIntroduction /|rDidier Dubois /
|rSébastien Destercke --|g4.2.|tCapacities and n-
monotonicity /|rDidier Dubois /|rSébastien Destercke --
|g4.3.|t2-monotone capacities /|rDidier Dubois /
|rSébastien Destercke --|g4.4.|tProbability intervals on
singletons /|rDidier Dubois /|rSébastien Destercke --
|g4.5.|tinfinity-monotone capacities /|rDidier Dubois /
|rSébastien Destercke --|g4.5.1.|tConstructing infinity-
monotone capacities /|rDidier Dubois /|rSébastien
Destercke --|g4.5.2.|tSimple support functions /|rDidier
Dubois /|rSébastien Destercke --|g4.5.3.|tFurther elements
/|rDidier Dubois /|rSébastien Destercke --|g4.6.
|tPossibility distributions, p-boxes, clouds and related
models /|rSébastien Destercke /|rDidier Dubois --|g4.6.1.
|tPossibility distributions /|rDidier Dubois /|rSébastien
Destercke --|g4.6.2.|tFuzzy intervals /|rDidier Dubois /
|rSébastien Destercke --|g4.6.3.|tClouds /|rDidier Dubois
/|rSébastien Destercke --|g4.6.4.|tp-boxes /|rDidier
Dubois /|rSébastien Destercke --|g4.7.|tNeighbourhood
models /|rDidier Dubois /|rSébastien Destercke --|g4.7.1.
|tPari-mutuel /|rDidier Dubois /|rSébastien Destercke --
|g4.7.2.|tOdds-ratio /|rDidier Dubois /|rSébastien
Destercke --|g4.7.3.|tLinear-vacuous /|rDidier Dubois /
|rSébastien Destercke --|g4.7.4.|tRelations between
neighbourhood models /|rDidier Dubois /|rSébastien
Destercke --|g4.8.|tSummary /|rDidier Dubois /|rSébastien
Destercke --|g5.1.|tImprecise probability = modal logic +
probability /|rDidier Dubois /|rSébastien Destercke --
|g5.1.1.|tBoolean possibility theory and modal logic /
|rDidier Dubois /|rSébastien Destercke --|g5.1.2.
|tunifying framework for capacity based uncertainty
theories /|rDidier Dubois /|rSébastien Destercke --|g5.2.
|tFrom imprecise probabilities to belief functions and
possibility theory /|rDidier Dubois /|rSébastien Destercke
--|g5.2.1.|tRandom disjunctive sets /|rDidier Dubois /
|rSébastien Destercke --|g5.2.2.|tNumerical possibility
theory /|rDidier Dubois /|rSébastien Destercke --|g5.2.3.
|tOverall picture /|rDidier Dubois /|rSébastien Destercke
--|g5.3.|tDiscrepancies between uncertainty theories /
|rDidier Dubois /|rSébastien Destercke --|g5.3.1.
|tObjectivist vs.
505 00 |rSubjectivist standpoints /|rSébastien Destercke /
|rDidier Dubois --|g5.3.2.|tDiscrepancies in conditioning
/|rSébastien Destercke /|rDidier Dubois --|g5.3.3.
|tDiscrepancies in notions of independence /|rSébastien
Destercke /|rDidier Dubois --|g5.3.4.|tDiscrepancies in
fusion operations /|rSébastien Destercke /|rDidier Dubois
--|g5.4.|tFurther reading /|rDidier Dubois /|rSébastien
Destercke --|g6.1.|tIntroduction /|rVladimir Vovk /|rGlenn
Shafer --|g6.2.|tlaw of large numbers /|rGlenn Shafer /
|rVladimir Vovk --|g6.3.|tgeneral forecasting protocol /
|rVladimir Vovk /|rGlenn Shafer --|g6.4.|taxiom of
continuity /|rVladimir Vovk /|rGlenn Shafer --|g6.5.
|tDoob's argument /|rVladimir Vovk /|rGlenn Shafer --
|g6.6.|tLimit theorems of probability /|rVladimir Vovk /
|rGlenn Shafer --|g6.7.|tLévy's zero-one law /|rVladimir
Vovk /|rGlenn Shafer --|g6.8.|taxiom of continuity
revisited /|rGlenn Shafer /|rVladimir Vovk --|g6.9.
|tFurther reading /|rVladimir Vovk /|rGlenn Shafer --
|tAcknowledgements /|rVladimir Vovk /|rGlenn Shafer --
|g7.1.|tBackground and introduction /|rThomas Augustin /
|rGero Walter /|rFrank P.A. Coolen --|g7.1.1.|tWhat is
statistical inference? /|rThomas Augustin /|rGero Walter /
|rFrank P.A. Coolen --|g7.1.2.|t(Parametric) statistical
models and i.i.d. samples /|rThomas Augustin /|rGero
Walter /|rFrank P.A. Coolen --|g7.1.3.|tBasic tasks and
procedures of statistical inference /|rThomas Augustin /
|rGero Walter /|rFrank P.A. Coolen --|g7.1.4.|tSome
methodological distinctions /|rThomas Augustin /|rGero
Walter /|rFrank P.A. Coolen --|g7.1.5.|tExamples:
Multinomial and normal distribution /|rThomas Augustin /
|rGero Walter /|rFrank P.A. Coolen --|g7.2.|tImprecision
in statistics, some general sources and motives /|rThomas
Augustin /|rGero Walter /|rFrank P.A. Coolen --|g7.2.1.
|tModel and data imprecision; sensitivity analysis and
ontological views on imprecision /|rThomas Augustin /
|rGero Walter /|rFrank P.A. Coolen --|g7.2.2.|trobustness
shock, sensitivity analysis /|rThomas Augustin /|rGero
Walter /|rFrank P.A. Coolen --|g7.2.3.|tImprecision as a
modelling tool to express the quality of partial knowledge
/|rGero Walter /|rFrank P.A. Coolen /|rThomas Augustin --
|g7.2.4.|tlaw of decreasing credibility /|rThomas Augustin
/|rGero Walter /|rFrank P.A. Coolen --|g7.2.5.|tImprecise
sampling models: Typical models and motives /|rThomas
Augustin /|rGero Walter /|rFrank P.A. Coolen --|g7.3.
|tSome basic concepts of statistical models relying on
imprecise probabilities /|rGero Walter /|rThomas Augustin
/|rFrank P.A. Coolen --|g7.3.1.|tMost common classes of
models and notation /|rThomas Augustin /|rGero Walter /
|rFrank P.A. Coolen --|g7.3.2.|tImprecise parametric
statistical models and corresponding i.i.d. samples /
|rThomas Augustin /|rGero Walter /|rFrank P.A. Coolen --
|g7.4.|tGeneralized Bayesian inference /|rThomas Augustin
/|rGero Walter /|rFrank P.A. Coolen --|g7.4.1.|tSome
selected results from traditional Bayesian statistics /
|rGero Walter /|rThomas Augustin /|rFrank P.A. Coolen --
|g7.4.2.|tSets of precise prior distributions, robust
Bayesian inference and the generalized Bayes rule /
|rThomas Augustin /|rGero Walter /|rFrank P.A. Coolen.
505 00 |gNote continued:|g7.4.3.|tcloser exemplary look at a
popular class of models: The IDM and other models based on
sets of conjugate priors in exponential families /|rThomas
Augustin /|rGero Walter /|rFrank P.A. Coolen --|g7.4.4.
|tSome further comments and a brief look at other models
for generalized Bayesian inference /|rThomas Augustin /
|rGero Walter /|rFrank P.A. Coolen --|g7.5.|tFrequentist
statistics with imprecise probabilities /|rThomas Augustin
/|rGero Walter /|rFrank P.A. Coolen --|g7.5.1.
|tnonrobustness of classical frequentist methods /|rThomas
Augustin /|rGero Walter /|rFrank P.A. Coolen --|g7.5.2.
|t(Frequentist) hypothesis testing under imprecise
probability: Huber-Strassen theory and extensions /
|rThomas Augustin /|rGero Walter /|rFrank P.A. Coolen --
|g7.5.3.|tTowards a frequentist estimation theory under
imprecise probabilities -- some basic criteria and first
results /|rThomas Augustin /|rGero Walter /|rFrank P.A.
Coolen --|g7.5.4.|tbrief outlook on frequentist methods /
|rThomas Augustin /|rGero Walter /|rFrank P.A. Coolen --
|g7.6.|tNonparametric predictive inference /|rThomas
Augustin /|rFrank P.A. Coolen /|rGero Walter --|g7.6.1.
|tOverview /|rThomas Augustin /|rFrank P.A. Coolen /|rGero
Walter --|g7.6.2.|tApplications and challenges /|rThomas
Augustin /|rFrank P.A. Coolen /|rGero Walter --|g7.7.
|tbrief sketch of some further approaches and aspects /
|rThomas Augustin /|rFrank P.A. Coolen /|rGero Walter --
|g7.8.|tData imprecision, partial identification /|rThomas
Augustin /|rFrank P.A. Coolen /|rGero Walter --|g7.8.1.
|tData imprecision /|rThomas Augustin /|rFrank P.A. Coolen
/|rGero Walter --|g7.8.2.|tCautious data completion /
|rThomas Augustin /|rFrank P.A. Coolen /|rGero Walter --
|g7.8.3.|tPartial identification and observationally
equivalent models /|rThomas Augustin /|rFrank P.A. Coolen
/|rGero Walter --|g7.8.4.|tbrief outlook on some further
aspects /|rThomas Augustin /|rFrank P.A. Coolen /|rGero
Walter --|g7.9.|tSome general further reading /|rThomas
Augustin /|rFrank P.A. Coolen /|rGero Walter --|g7.10.
|tSome general challenges /|rThomas Augustin /|rFrank P.A.
Coolen /|rGero Walter --|tAcknowledgements /|rThomas
Augustin /|rFrank P.A. Coolen /|rGero Walter --|g8.1.|tNon
-sequential decision problems /|rNathan Huntley /
|rMatthias C.M. Troffaes /|rRobert Hable --|g8.1.1.
|tChoosing from a set of gambles /|rNathan Huntley /
|rMatthias C.M. Troffaes /|rRobert Hable --|g8.1.2.
|tChoice functions for coherent lower previsions /|rNathan
Huntley /|rMatthias C.M. Troffaes /|rRobert Hable --|g8.2.
|tSequential decision problems /|rNathan Huntley /
|rMatthias C.M. Troffaes /|rRobert Hable --|g8.2.1.
|tStatic sequential solutions: Normal form /|rNathan
Huntley /|rMatthias C.M. Troffaes /|rRobert Hable --
|g8.2.2.|tDynamic sequential solutions: Extensive form /
|rNathan Huntley /|rMatthias C.M. Troffaes /|rRobert Hable
--|g8.3.|tExamples and applications /|rRobert Hable /
|rNathan Huntley /|rMatthias C.M. Troffaes --|g8.3.1.
|tEllsberg's paradox /|rNathan Huntley /|rMatthias C.M.
Troffaes /|rRobert Hable --|g8.3.2.|tRobust Bayesian
statistics /|rNathan Huntley /|rMatthias C.M. Troffaes /
|rRobert Hable --|g9.1.|tIntroduction /|rAlessandro
Antonucci /|rMarco Zaffalon /|rCassio P. de Campos --
|g9.2.|tCredal sets /|rAlessandro Antonucci /|rMarco
Zaffalon /|rCassio P. de Campos --|g9.2.1.|tDefinition and
relation with lower previsions /|rAlessandro Antonucci /
|rMarco Zaffalon /|rCassio P. de Campos --|g9.2.2.
|tMarginalization and conditioning /|rAlessandro Antonucci
/|rMarco Zaffalon /|rCassio P. de Campos --|g9.2.3.
|tComposition /|rAlessandro Antonucci /|rMarco Zaffalon /
|rCassio P. de Campos --|g9.3.|tIndependence /|rAlessandro
Antonucci /|rCassio P. de Campos /|rMarco Zaffalon --
|g9.4.|tCredal networks /|rAlessandro Antonucci /|rMarco
Zaffalon /|rCassio P. de Campos --|g9.4.1.|tNonseparately
specified credal networks /|rAlessandro Antonucci /|rMarco
Zaffalon /|rCassio P. de Campos --|g9.5.|tComputing with
credal networks /|rAlessandro Antonucci /|rMarco Zaffalon
/|rCassio P. de Campos --|g9.5.1.|tCredal networks
updating /|rAlessandro Antonucci /|rMarco Zaffalon /
|rCassio P. de Campos --|g9.5.2.|tModelling and updating
with missing data /|rAlessandro Antonucci /|rMarco
Zaffalon /|rCassio P. de Campos --|g9.5.3.|tAlgorithms for
credal networks updating /|rAlessandro Antonucci /|rMarco
Zaffalon /|rCassio P. de Campos --|g9.5.4.|tInference on
credal networks as a multilinear programming task /
|rAlessandro Antonucci /|rMarco Zaffalon /|rCassio P. de
Campos --|g9.6.|tFurther reading /|rAlessandro Antonucci /
|rMarco Zaffalon /|rCassio P. de Campos --
|tAcknowledgements /|rAlessandro Antonucci /|rMarco
Zaffalon /|rCassio P.
505 00 |rDe Campos --|g10.1.|tIntroduction /|rGiorgio Corani /
|rJoaquin Abellán /|rMarco Zaffalon /|rSerafin Moral /
|rAndrés Masegosa --|g10.2.|tNaive Bayes /|rGiorgio Corani
/|rJoaquin Abellán /|rMarco Zaffalon /|rSerafin Moral /
|rAndrés Masegosa --|g10.2.1.|tDerivation of naive Bayes /
|rJoaquin Abellán /|rAndrés Masegosa /|rGiorgio Corani /
|rMarco Zaffalon /|rSerafin Moral --|g10.3.|tNaive credal
classifier (NCC) /|rGiorgio Corani /|rJoaquin Abellán /
|rMarco Zaffalon /|rSerafin Moral /|rAndrés Masegosa --
|g10.3.1.|tChecking Credal-dominance /|rGiorgio Corani /
|rJoaquin Abellán /|rMarco Zaffalon /|rSerafin Moral /
|rAndrés Masegosa --|g10.3.2.|tParticular behaviours of
NCC /|rGiorgio Corani /|rJoaquin Abellán /|rMarco Zaffalon
/|rSerafin Moral /|rAndrés Masegosa --|g10.3.3.|tNCC2:
Conservative treatment of missing data /|rGiorgio Corani /
|rJoaquin Abellán /|rMarco Zaffalon /|rSerafin Moral /
|rAndrés Masegosa --|g10.4.|tExtensions and developments
of the naive credal classifier /|rGiorgio Corani /
|rJoaquin Abellán /|rMarco Zaffalon /|rSerafin Moral /
|rAndrés Masegosa --|g10.4.1.|tLazy naive credal
classifier /|rGiorgio Corani /|rJoaquin Abellán /|rMarco
Zaffalon /|rSerafin Moral /|rAndrés Masegosa --|g10.4.2.
|tCredal model averaging /|rGiorgio Corani /|rJoaquin
Abellán /|rMarco Zaffalon /|rSerafin Moral /|rAndrés
Masegosa --|g10.4.3.|tProfile-likelihood classifiers /
|rGiorgio Corani /|rJoaquin Abellán /|rMarco Zaffalon /
|rSerafin Moral /|rAndrés Masegosa --|g10.4.4.|tTree-
augmented networks (TAN) /|rGiorgio Corani /|rJoaquin
Abellán /|rMarco Zaffalon /|rSerafin Moral /|rAndrés
Masegosa --|g10.5.|tTree-based credal classifiers /
|rGiorgio Corani /|rJoaquin Abellán /|rMarco Zaffalon /
|rSerafin Moral /|rAndrés Masegosa --|g10.5.1.
|tUncertainty measures on credal sets: The maximum entropy
function /|rGiorgio Corani /|rJoaquin Abellán /|rMarco
Zaffalon /|rSerafin Moral /|rAndrés Masegosa --|g10.5.2.
|tObtaining conditional probability intervals with the
imprecise Dirichlet model /|rGiorgio Corani /|rJoaquin
Abellán /|rMarco Zaffalon /|rSerafin Moral /|rAndrés
Masegosa --|g10.5.3.|tClassification procedure /|rGiorgio
Corani /|rJoaquin Abellán /|rMarco Zaffalon /|rSerafin
Moral /|rAndrés Masegosa --|g10.6.|tMetrics, experiments
and software /|rGiorgio Corani /|rJoaquin Abellán /|rMarco
Zaffalon /|rSerafin Moral /|rAndrés Masegosa --|g10.7.
|tScoring the conditional probability of the class /
|rGiorgio Corani /|rJoaquin Abellán /|rMarco Zaffalon /
|rSerafin Moral /|rAndrés Masegosa --|g10.7.1.|tSoftware /
|rGiorgio Corani /|rJoaquin Abellán /|rAndrés Masegosa /
|rSerafin Moral /|rMarco Zaffalon --|g10.7.2.|tExperiments
/|rMarco Zaffalon /|rSerafin Moral /|rAndrés Masegosa /
|rJoaquin Abellán /|rGiorgio Corani --|g10.7.3.
|tExperiments comparing conditional probabilities of the
class /|rSerafin Moral /|rMarco Zaffalon /|rAndrés
Masegosa /|rJoaquin Abellán /|rGiorgio Corani --
|tAcknowledgements /|rSerafin Moral /|rAndrés Masegosa /
|rJoaquin Abellán /|rGiorgio Corani /|rMarco Zaffalon --
|g11.1.|tclassical characterization of stochastic
processes /|rFilip Herman /|rDamjan [Š]kluj --|g11.1.1.
|tBasic definitions /|rFilip Herman /|rDamjan [Š]kluj --
|g11.1.2.|tPrecise Markov chains /|rFilip Herman /|rDamjan
[Š]kluj --|g11.2.|tEvent-driven random processes /|rFilip
Herman /|rDamjan [Š]kluj --|g11.3.|tImprecise Markov
chains /|rFilip Herman /|rDamjan [Š]kluj --|g11.3.1.|tFrom
precise to imprecise Markov chains /|rFilip Herman /
|rDamjan [Š]kluj --|g11.3.2.|tImprecise Markov models
under epistemic irrelevance /|rFilip Herman /|rDamjan
[Š]kluj --|g11.3.3.|tImprecise Markov models under strong
independence /|rFilip Herman /|rDamjan [Š]kluj --|g11.3.4.
|tWhen does the interpretation of independence (not)
matter? /|rFilip Herman /|rDamjan [Š]kluj --|g11.4.|tLimit
behaviour of imprecise Markov chains /|rFilip Herman /
|rDamjan [Š]kluj --|g11.4.1.|tMetric properties of
imprecise probability models /|rFilip Herman /|rDamjan
[Š]kluj --|g11.4.2.|tPerron-Frobenius theorem /|rFilip
Herman /|rDamjan [Š]kluj --|g11.4.3.|tInvariant
distributions /|rFilip Herman /|rDamjan [Š]kluj --
|g11.4.4.|tCoefficients of ergodicity /|rFilip Herman /
|rDamjan [Š]kluj --|g11.4.5.|tCoefficients of ergodicity
for imprecise Markov chains /|rFilip Herman /|rDamjan
[Š]kluj --|g11.5.|tFurther reading /|rDamjan [Š]kluj /
|rFilip Herman --|g12.1.|tIntroduction /|rPaolo Vicig --
|g12.2.|tImprecise previsions and betting /|rPaolo Vicig -
-|g12.3.|tImprecise previsions and risk measurement /
|rPaolo Vicig --|g12.3.1.|tRisk measures as imprecise
previsions /|rPaolo Vicig --|g12.3.2.|tCoherent risk
measures /|rPaolo Vicig --|g12.3.3.|tConvex risk measures
(and previsions) /|rPaolo Vicig --|g12.4.|tFurther reading
/|rPaolo Vicig --|g13.1.|tIntroduction /|rMichael
Oberguggenberger --|g13.2.|tProbabilistic dimensioning in
a simple example /|rMichael Oberguggenberger --|g13.3.
|tRandom set modelling of the output variability /
|rMichael Oberguggenberger --|g13.4.|tSensitivity analysis
/|rMichael Oberguggenberger.
520 "In recent years, the theory has become widely accepted
and has been further developed, but a detailed
introduction is needed in order to make the material
available and accessible to a wide audience. This will be
the first book providing such an introduction, covering
core theory and recent developments which can be applied
to many application areas. All authors of individual
chapters are leading researchers on the specific topics,
assuring high quality and up-to-date contents. An
Introduction to Imprecise Probabilities provides a
comprehensive introduction to imprecise probabilities,
including theory and applications reflecting the current
state if the art. Each chapter is written by experts on
the respective topics, including: Sets of desirable
gambles; Coherent lower (conditional) previsions; Special
cases and links to literature; Decision making; Graphical
models; Classification; Reliability and risk assessment;
Statistical inference; Structural judgments; Aspects of
implementation (including elicitation and computation);
Models in finance; Game-theoretic probability; Stochastic
processes (including Markov chains); Engineering
applications. Essential reading for researchers in
academia, research institutes and other organizations, as
well as practitioners engaged in areas such as risk
analysis and engineering"--|cProvided by publisher
520 "Provides a comprehensive introduction to imprecise
probabilities, including theory and applications
reflecting the current state of the art"--|cProvided by
publisher
588 0 Print version record and CIP data provided by publisher.
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Probabilities.
650 6 Probabilités.
650 7 probability.|2aat
650 7 Probabilities|2fast
700 1 Augustin, Thomas,|eeditor.
776 08 |iPrint version:|tIntroduction to imprecise probabilities.
|dHoboken, NJ : John Wiley & Sons Inc., 2014
|z9780470973813|w(DLC) 2013041146|w(OCoLC)851413880
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781118763148/?ar
|zAvailable on O'Reilly for Public Libraries
880 00 |6505-01/(S|gContents note continued:|g13.5.|tHybrid
models /|rMichael Oberguggenberger --|g13.6.|tReliability
analysis and decision making in engineering /|rMichael
Oberguggenberger --|g13.7.|tFurther reading /|rMichael
Oberguggenberger --|g14.1.|tIntroduction /|rLev V. Utkin /
|rFrank P.A. Coolen --|g14.2.|tStress-strength reliability
/|rLev V. Utkin /|rFrank P.A. Coolen --|g14.3.
|tStatistical inference in reliability and risk /|rLev V.
Utkin /|rFrank P.A. Coolen --|g14.4.|tNonparametric
predictive inference in reliability and risk /|rLev V.
Utkin /|rFrank P.A. Coolen --|g14.5.|tDiscussion and
research challenges /|rLev V. Utkin /|rFrank P.A. Coolen -
-|g15.1.|tMethods and issues /|rMichael Smithson --|g15.2.
|tEvaluating imprecise probability judgements /|rMichael
Smithson --|g15.3.|tFactors affecting elicitation /
|rMichael Smithson --|g15.4.|tMatching methods with
purposes /|rMichael Smithson --|g15.5.|tFurther reading /
|rMichael Smithson --|g16.1.|tIntroduction /|rRobert Hable
/|rMatthias C.M. Troffaes --|g16.2.|tNatural extension /
|rRobert Hable /|rMatthias C.M. Troffaes --|g16.2.1.
|tConditional lower previsions with arbitrary domains /
|rRobert Hable /|rMatthias C.M. Troffaes --|g16.2.2.
|tWalley-Pelessoni-Vicig algorithm /|rRobert Hable /
|rMatthias C.M. Troffaes --|g16.2.3.|tChoquet integration
/|rRobert Hable /|rMatthias C.M. Troffaes --|g16.2.4.
|tMöbius inverse /|rRobert Hable /|rMatthias C.M. Troffaes
--|g16.2.5.|tLinear-vacuous mixture /|rRobert Hable /
|rMatthias C.M. Troffaes --|g16.3.|tDecision making /
|rRobert Hable /|rMatthias C.M. Troffaes --|g16.3.1.|tΓ-
maximin, Γ-maximax and Hurwicz /|rRobert Hable /|rMatthias
C.M. Troffaes --|g16.3.2.|tMaximality /|rRobert Hable /
|rMatthias C.M. Troffaes --|g16.3.3.|tE-admissibility /
|rRobert Hable /|rMatthias C.M. Troffaes --|g16.3.4.
|tInterval dominance /|rRobert Hable /|rMatthias C.M.
Troffaes.
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