2018
Direct likelihood-based inference for discretely observed stochastic compartmental models of infectious disease
Ho L, Crawford F, Suchard M. Direct likelihood-based inference for discretely observed stochastic compartmental models of infectious disease. The Annals Of Applied Statistics 2018, 12: 1993-2021. DOI: 10.1214/18-aoas1141.Peer-Reviewed Original ResearchHamiltonian Monte CarloStochastic compartmental modelMonte CarloLikelihood-based inferenceSequential Monte CarloCompartmental modelObserved dataMathematical foundationHigh computational costLoss of accuracyLikelihood evaluationBayesian inferenceSIR modelBroad classRecursion methodComputational costRemoved (SIR) modelEfficient algorithmTransition probabilitiesCarloModel assumptionsInfectious disease epidemicsInferenceCentury plagueApproximation
2017
Birth/birth-death processes and their computable transition probabilities with biological applications
Ho LST, Xu J, Crawford FW, Minin VN, Suchard MA. Birth/birth-death processes and their computable transition probabilities with biological applications. Journal Of Mathematical Biology 2017, 76: 911-944. PMID: 28741177, PMCID: PMC5783825, DOI: 10.1007/s00285-017-1160-3.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAnimalsBayes TheoremCommunicable DiseasesComputational BiologyComputer SimulationEnglandEpidemicsHistory, 17th CenturyHost-Parasite InteractionsHumansLikelihood FunctionsMarkov ChainsMathematical ConceptsModels, BiologicalMonte Carlo MethodPlagueProbabilityStochastic ProcessesConceptsBirth-death processTransition probabilitiesFinite-time transition probabilitiesSIR modelMonte Carlo approximationJoint posterior distributionLikelihood-based inferenceApproximate Bayesian computationStatistical inferenceMatrix exponentiationPosterior distributionProcess approximationBivariate extensionBayesian computationFraction representationLaplace transformCorrelation structureUnivariate populationsRemoved (SIR) modelSmall systemsBivariate processEfficient algorithmApproximationDirect inferenceFast method
2010
Bayesian and likelihood-based inference for the bivariate normal correlation coefficient
Ghosh M, Mukherjee B, Santra U, Kim D. Bayesian and likelihood-based inference for the bivariate normal correlation coefficient. Journal Of Statistical Planning And Inference 2010, 140: 1410-1416. DOI: 10.1016/j.jspi.2009.11.013.Peer-Reviewed Original ResearchAsymptotic matching of coverage probabilities of Bayesian credible intervalsCoverage probabilities of Bayesian credible intervalsHighest posterior density matchingInversion of test statisticsCredible intervalsLikelihood-based inferenceBayesian credible intervalsBivariate normal distributionLikelihood-based methodsTest statisticsNormal distributionQuantile matchingSimulation studyAsymptotic matchingDensity matchingInferencePriorsIntervalMatching criterionCoefficientProbability
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