2025
A Bayesian Approach to the G‐Formula via Iterative Conditional Regression
Liu R, Hu L, Wilson F, Warren J, Li F. A Bayesian Approach to the G‐Formula via Iterative Conditional Regression. Statistics In Medicine 2025, 44: e70123. PMID: 40476299, PMCID: PMC12184534, DOI: 10.1002/sim.70123.Peer-Reviewed Original ResearchConceptsCausal effect estimationTime-varying covariatesModel misspecification biasBayesian approachReal world data examplesG-formulaAverage causal effect estimationTime-varying treatmentsBayesian additive regression treesAverage causal effectAdditive regression treesConditional expectationOutcome regressionConditional distributionJoint distributionData examplesPosterior distributionMisspecification biasParametric regressionSimulation studyEffect estimatesSampling algorithmAlgorithm formulaCausal effectsFlexible machine learning techniques
2024
Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion-based Bayesian posterior distribution prediction of kinetic parameters in dynamic PET. 2024, 00: 1-1. DOI: 10.1109/nss/mic/rtsd57108.2024.10657955.Peer-Reviewed Original ResearchConditional variational autoencoderEfficient deep learning-based approachMarkov chain Monte CarloDenoising diffusion probabilistic modelDeep learning-based approachDiffusion probabilistic modelLearning-based approachApproximate posterior distributionPosterior distributionVariational autoencoderHeavy computationTau protein aggregationBayesian inferenceProbabilistic modelData-DrivenStudy molecular processesBayesian posterior distributionProtein aggregationMetropolis-Hastings Markov chain Monte CarloMolecular processesAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersEstimate posterior distributionsAutoencoderHematocrits of red blood cell units increase during storage due to changes in mean corpuscular volume, impacting outcomes of red cell exchange procedures
Lee E, Musante K, Errico J, Rinder H, Kleinstein S, Tormey C, Yurtsever N. Hematocrits of red blood cell units increase during storage due to changes in mean corpuscular volume, impacting outcomes of red cell exchange procedures. American Journal Of Clinical Pathology 2024, 162: s157-s157. DOI: 10.1093/ajcp/aqae129.348.Peer-Reviewed Original ResearchMean corpuscular volumeMultilevel linear regression modelsPosterior meanStorage timeWeeks of storageRed blood cell exchangeFunction of storage timeLinear regression modelsRed blood cell unitsPosterior distribution of model parametersAkaike weightsMultilevel regression modelsRegression modelsRed blood cellsPosterior distributionDistribution of model parametersRBC unitsModel selectionRStan packageCorpuscular volumeHematological parametersDiffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET
Djebra Y, Liu X, Marin T, Tiss A, Dhaynaut M, Guehl N, Johnson K, Fakhri G, Ma C, Ouyang J. Diffusion Model-Based Posterior Distribution Prediction for Kinetic Parameter Estimation in Dynamic PET. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2024, 00: 1-5. PMID: 39530051, PMCID: PMC11554386, DOI: 10.1109/isbi56570.2024.10635805.Peer-Reviewed Original ResearchPosterior distributions of kinetic parametersDenoising diffusion probabilistic modelHyperphosphorylated tauP-tauDiffusion probabilistic modelAlzheimer's diseaseNeurodegenerative diseasesKinetic parametersPosterior distributionInference efficiencyComputational needsEstimate kinetic parametersProbabilistic modelComputation timerBMA: A robust Bayesian Model Averaging Method for phase II basket trials based on informative mixture priors
Wang X, Wei W. rBMA: A robust Bayesian Model Averaging Method for phase II basket trials based on informative mixture priors. Contemporary Clinical Trials 2024, 140: 107505. PMID: 38521384, DOI: 10.1016/j.cct.2024.107505.Peer-Reviewed Original ResearchConceptsPhase II basket trialRobust Bayesian modelBasket trial designBiomarker-defined subgroupsMixture priorsBinary endpointsModel averaging methodPosterior distributionBayesian modelBasket trialsSimulation studyEra of targeted therapyDevelopment of targeted agentsPriorsAverage methodGenomic alterationsPatient populationNovel treatmentAntitumor activityPosterior weightsEarly phase oncology trialsStatistical powerOncology trialsTrial designTrials
2023
Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey
Ghosh A, Urry C, Mishra A, Perreault-Levasseur L, Natarajan P, Sanders D, Nagai D, Tian C, Cappelluti N, Kartaltepe J, Powell M, Rau A, Treister E. Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey. The Astrophysical Journal 2023, 953: 134. DOI: 10.3847/1538-4357/acd546.Peer-Reviewed Original ResearchSource codeMachine-learning algorithmsMachine-learning frameworkData setsTransfer learningEstimation networkPrevious stateProfile fitting algorithmReal dataNancy Grace Roman Space TelescopeRoman Space TelescopeLarge imaging surveysAlgorithmFitting algorithmUncertainty quantificationSimulations of galaxiesBayesian posteriorPosterior distributionExternal cataloguesFirst trainingSpace TelescopeGalaxy bulgesLight ratioSignificant improvementGalaxies
2022
Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography
Liu X, Marin T, Amal T, Woo J, Fakhri G, Ouyang J. Posterior estimation using deep learning: a simulation study of compartmental modeling in dynamic positron emission tomography. Medical Physics 2022, 50: 1539-1548. PMID: 36331429, PMCID: PMC10087283, DOI: 10.1002/mp.16078.Peer-Reviewed Original ResearchConceptsConditional variational auto-encoderDeep learning approachNeural networkDeep learningMarkov chain Monte CarloVariational Bayesian inference frameworkLearning approachDeep learning-based approachVariational auto-encoderDeep neural networksLearning-based approachDynamic brain PET imagingPosterior distributionEstimate posterior distributionsBayesian inference frameworkAuto-encoderMedical imagesInference frameworkNetworkSimulation studyBrain PET imagingLearningPosterior estimatesInferior performanceImages
2021
Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data
Zhao Y, Chang C, Hannum M, Lee J, Shen R. Bayesian network-driven clustering analysis with feature selection for high-dimensional multi-modal molecular data. Scientific Reports 2021, 11: 5146. PMID: 33664338, PMCID: PMC7933297, DOI: 10.1038/s41598-021-84514-0.Peer-Reviewed Original ResearchConceptsMolecular dataJoint posterior distributionHigh-dimensional settingsVariational Bayes approachSingle-cell dataArt clustering methodsPosterior distributionMolecular profiling dataComputational efficiencyCanonical oncogenicTranscriptomic alterationsBiological discoveryModel inferenceBayes approachCell decompositionStemness phenotypeProfiling dataSingle cellsComputational methodsBulk tumorPathway alterationsNebulaClustering methodAnalysis settingsCell data
2020
embarcadero: Species distribution modelling with Bayesian additive regression trees in r
Carlson C. embarcadero: Species distribution modelling with Bayesian additive regression trees in r. Methods In Ecology And Evolution 2020, 11: 850-858. DOI: 10.1111/2041-210x.13389.Peer-Reviewed Original ResearchSpecies distribution modelsBayesian additive regression treesAdditive regression treesDistribution modelVariable selection procedureSets of treesRegression treesAutomated variable selection procedurePosterior distributionDisease transmission riskEcological applicationsEcological problemsRegression tree methodBinary outcomesSelection procedureTreesSpeciesEcologistsProbabilityPriorsClassification probabilities
2018
Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models
Zimmer C, Leuba SI, Cohen T, Yaesoubi R. Accurate quantification of uncertainty in epidemic parameter estimates and predictions using stochastic compartmental models. Statistical Methods In Medical Research 2018, 28: 3591-3608. PMID: 30428780, PMCID: PMC6517086, DOI: 10.1177/0962280218805780.Peer-Reviewed Original ResearchConceptsFilter degeneracyParameter estimatesPosterior distributionStochastic transmission-dynamic modelParameter posterior distributionsEpidemic compartmental modelKey epidemic parametersStochastic compartmental modelStochastic systemsPrediction intervalsCompartmental modelMultiple shootingArt calibration methodsEpidemic parametersDegeneracyDynamic modelInfluenza modelMSS approachLong-term predictionTransmission dynamic modelSimulation experimentsCalibration methodUncertaintyEstimatesCompetitive performanceEstimating Causal Effects on Social Networks
Forastiere L, Mealli F, Wu A, Airoldi E. Estimating Causal Effects on Social Networks. 2018, 00: 60-69. DOI: 10.1109/dsaa.2018.00016.Peer-Reviewed Original ResearchPosterior distributionPropensity score estimationBayesian procedureObserved networkStructural assumptionsNeighborhood treatmentsGeneralized propensity scoreNeighborhood interferenceRandom effectsSpline regressionNeighbor treatmentsCausal estimandsCommunity detection algorithmsScore estimationConnected unitsEstimationModel feedbackAssumptionUnmeasured confounding variablesPosterior Predictive $p$-Values with Fisher Randomization Tests in Noncompliance Settings: Test Statistics vs Discrepancy Measures
Forastiere L, Mealli F, Miratrix L. Posterior Predictive $p$-Values with Fisher Randomization Tests in Noncompliance Settings: Test Statistics vs Discrepancy Measures. Bayesian Analysis 2018, 13 DOI: 10.1214/17-ba1062.Peer-Reviewed Original ResearchDiscrepancy measureClassical test statisticsTest statisticIncorrect model specificationPosterior credible intervalsPosterior distributionFisher randomization testModel misspecificationCompliance typeImputation stepAverage causal effectCredible intervalsPermutation testComplier average causal effectGeneral schemeAdditional modelingModel specificationStatisticsRandomization testMisspecificationDifferent approachesOverall approachSchemeValidityRandomized experiments
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
2016
PhyInformR: phylogenetic experimental design and phylogenomic data exploration in R
Dornburg A, Fisk JN, Tamagnan J, Townsend JP. PhyInformR: phylogenetic experimental design and phylogenomic data exploration in R. BMC Ecology And Evolution 2016, 16: 262. PMID: 27905871, PMCID: PMC5134231, DOI: 10.1186/s12862-016-0837-3.Peer-Reviewed Original ResearchConceptsParallel processingOpen-source software packageBayesian posterior distributionLack of softwareOpen-source programPhylogenetic experimental designProbability of resolutionUser hardwareData explorationData visualizationNext-generation sequence datasetsInformation contentSource programNovel visualizationPosterior distributionBayesian settingAdditional sample dataPower usersDataset partitionsBayesian approachSequence data setsSoftware packageDatasetGenomic information contentSoftwareValidation of Bayesian analysis of compartmental kinetic models in medical imaging
Sitek A, Li Q, Fakhri G, Alpert N. Validation of Bayesian analysis of compartmental kinetic models in medical imaging. Physica Medica 2016, 32: 1252-1258. PMID: 27692754, PMCID: PMC5720163, DOI: 10.1016/j.ejmp.2016.09.010.Peer-Reviewed Original ResearchConceptsAccurate estimation of uncertaintyComputer simulationsMedical imagesPosterior distributionDistributed noiseTime series of imagesClosed-formSeries of imagesData setsKinetic parametersMarkov chain Monte Carlo methodsPosterior distributions of kinetic parametersNon-linear least squares methodAccurate estimationComputerLeast-squares methodKinetic modelEstimation of kinetic parametersF18-fluorodeoxyglucoseBayesian estimationImagesStatistical inferenceMonte Carlo methodEstimates of uncertaintyInformation- Bayesian ROC Methods
Zou K, Liu A, Bandos A, Ohno-Machado L, Rockette H. - Bayesian ROC Methods. 2016, 102-121. DOI: 10.1201/b11031-8.Peer-Reviewed Original Research
2013
Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference
Li S, Mukherjee B, Batterman S, Ghosh M. Bayesian Analysis of Time-Series Data under Case-Crossover Designs: Posterior Equivalence and Inference. Biometrics 2013, 69: 925-936. PMID: 24289144, PMCID: PMC4108592, DOI: 10.1111/biom.12102.Peer-Reviewed Original ResearchConceptsSemi-parametric Bayesian approachLikelihood-based approachRandom nuisance parametersTime series analysisFrequentist literatureNuisance parametersDirichlet processInferential issuesConditional likelihoodPosterior distributionRisk functionTime seriesBayesian workFrequentist approachCase-crossover designSimulation studyRestrictive assumptionsBayesian approachTime series dataLikelihood formulationBayesian methodsEquivalent resultsBayesian analysisCase-crossoverBayesian frameworkAddressing extrema and censoring in pollutant and exposure data using mixture of normal distributions
Li S, Batterman S, Su F, Mukherjee B. Addressing extrema and censoring in pollutant and exposure data using mixture of normal distributions. Atmospheric Environment 2013, 77: 464-473. PMID: 24348086, PMCID: PMC3857711, DOI: 10.1016/j.atmosenv.2013.05.004.Peer-Reviewed Original ResearchFinite mixture of normalsDirichlet process mixtureMixtures of normalsDirichlet process mixtures of normalsFinite mixtureHeavy tailsDirichlet process mixture methodsMethod detection limitsComprehensive simulation studyDistributions of VOC concentrationsProcess mixtureStandard model assumptionsPosterior distributionEmpirical densityNormal distributionSimulation studyGoodness-of-fit criteriaVolatile organic compoundsDensity estimationGoodness-of-fitDensity estimation methodCensoringConvergence issuesExposure dataEstimation methodEXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning
Wang S, Jiang X, Wu Y, Cui L, Cheng S, Ohno-Machado L. EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning. Journal Of Biomedical Informatics 2013, 46: 480-496. PMID: 23562651, PMCID: PMC3676314, DOI: 10.1016/j.jbi.2013.03.008.Peer-Reviewed Original ResearchConceptsHigh-level guaranteesOnline model learningSensitive informationModel learningEntire dataOnline learningAbsence of participantsMore flexibilitySame performanceExperimental resultsLearningCommunicationServerInformationGuaranteesModel updatingPosterior distributionServicesClientsUpdatingFrameworkFlexibilityModelPerformance
2011
Bayesian Time-Series Analysis of a Repeated-Measures Poisson Outcome With Excess Zeroes
Murphy TE, Van Ness PH, Araujo KL, Pisani MA. Bayesian Time-Series Analysis of a Repeated-Measures Poisson Outcome With Excess Zeroes. American Journal Of Epidemiology 2011, 174: 1230-1237. PMID: 22025357, PMCID: PMC3254157, DOI: 10.1093/aje/kwr252.Peer-Reviewed Original ResearchConceptsPosterior predictive simulationsExcess zerosBayesian modelBayesian time series analysisPredictive simulationsHierarchical Bayesian modelPoisson outcomesPosterior distributionTime series analysisBayesian frameworkRelated resultsStatistical factorsBayesian analysisRandom effects Poisson modelFrequentistZerosPoisson modelSmall samplesExcessive numberAutocorrelationSimulationsTime series techniquesModelPeriodicity
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