2018
Decoupling Combinatorial Complexity: a Two-Step Approach to Distributions of Runs
Kong Y. Decoupling Combinatorial Complexity: a Two-Step Approach to Distributions of Runs. Methodology And Computing In Applied Probability 2018, 21: 789-803. DOI: 10.1007/s11009-018-9689-1.Peer-Reviewed Original ResearchRun-related distributionsDistribution of runsMultivariate random sequencesCombinatorial complexityFinite Markov chainsMulti-object systemNearest-neighbor interactionsStatistical physicsCombinatorial difficultiesMarkov chainExplicit formRun statisticsNeighbor interactionsMultinomial coefficientsDifferent systematic approachesGeneral frameworkRun distributionRandom sequenceKinds of objectsTwo-step approachCombinatoricsGeneral formulaIndependent stepsComplexityPhysicsGenerating Function Methods for Run and Scan Statistics
Kong Y. Generating Function Methods for Run and Scan Statistics. 2018, 1-19. DOI: 10.1007/978-1-4614-8414-1_56-1.Peer-Reviewed Original ResearchFinite Markov chainsMulti-object systemNearest-neighbor interactionsStatistical physicsCombinatorial difficultiesGenerating functionMarkov chainExplicit formClassical resultsPattern statisticsNeighbor interactionsCombinatorial complexityFunction methodCombinatorial methodsKinds of objectsDifferent complexityMultiple objectsScan statisticIndividual objectsStatisticsIndependent stepsFirst stepWhole systemComplexityObjectsComputational methods for birth‐death processes
Crawford FW, Ho LST, Suchard MA. Computational methods for birth‐death processes. Wiley Interdisciplinary Reviews Computational Statistics 2018, 10 PMID: 29942419, PMCID: PMC6014701, DOI: 10.1002/wics.1423.Peer-Reviewed Original ResearchBirth-death processStatistical inferenceGeneral birth–death processesFinite-time transitionBasic mathematical theoryNon-negative integersContinuous-time Markov chainMaximum likelihood estimationMathematical theoryTheoretical propertiesComputational difficultiesProbability distributionMarkov chainAnalytic expressionsEM algorithmEquilibrium probabilityStatistical workLikelihood estimationSimple caseLinear processRich varietyComputational methodsSimple linear processSummary statisticsInference
2016
Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations
Studer L, Paulevé L, Zechner C, Reumann M, Martínez M, Koeppl H. Marginalized Continuous Time Bayesian Networks for Network Reconstruction from Incomplete Observations. Proceedings Of The AAAI Conference On Artificial Intelligence 2016, 30 DOI: 10.1609/aaai.v30i1.10294.Peer-Reviewed Original ResearchContinuous time Bayesian networksNoisy time series dataBayesian networkContinuous time Bayesian network modelsStructure learning problemEfficient parallel implementationSequential Monte Carlo schemeOptimal filtering problemTime series dataParameter estimation problemParallel implementationLearning problemsComplex network dynamicsPractical scenariosFiltering problemMonte Carlo schemeMarkov chainStochastic processEstimation problemNetwork dynamicsIncomplete observationsJoint stateNetworkNetwork reconstructionInference
2014
Estimation for General Birth-Death Processes
Crawford FW, Minin VN, Suchard MA. Estimation for General Birth-Death Processes. Journal Of The American Statistical Association 2014, 109: 730-747. PMID: 25328261, PMCID: PMC4196218, DOI: 10.1080/01621459.2013.866565.Peer-Reviewed Original ResearchBirth-death processGeneral birth–death processesConditional expectationE-stepEM algorithmLinear birth-death processContinuous-time Markov chainTransition probabilitiesClosed-form solutionLinear modelMaximum likelihood estimatesMaximum likelihood estimationTime-consuming simulationsStatistical inferenceCostly simulationsData augmentation procedureMarkov chainDiscrete timeEfficient computationLikelihood estimatesNumber of particlesFraction representationLaplace transformLikelihood estimationAlgorithm convergence
2012
Dynamics of prisoner's dilemma and the evolution of cooperation on networks
Manshadi V, Saberi A. Dynamics of prisoner's dilemma and the evolution of cooperation on networks. 2012, 227-235. DOI: 10.1145/2090236.2090256.Peer-Reviewed Original Research
2007
Computational Bayesian inference for estimating the size of a finite population
Nandram B, Zelterman D. Computational Bayesian inference for estimating the size of a finite population. Computational Statistics & Data Analysis 2007, 51: 2934-2945. DOI: 10.1016/j.csda.2006.11.034.Peer-Reviewed Original ResearchPosterior distributionComputational Bayesian inferenceBayesian log-linear modelsDiffuse prior distributionIntensive Bayesian methodsFinite populationMetropolis algorithmMarkov chainPrior distributionBayesian inferenceBayesian methodsComputational complexityLog-linear modelMarginal probabilitiesClosed populationAdministrative listsDistributionInferenceAlgorithmProbability
2002
Random Generation of Bayesian Networks
Ide J, Cozman F. Random Generation of Bayesian Networks. Lecture Notes In Computer Science 2002, 2507: 366-376. DOI: 10.1007/3-540-36127-8_35.Peer-Reviewed Original ResearchBayesian networkAcyclic graphConditional probability distributionNumber of arcsProbability distributionMarkov chainDirichlet distributionConditional distributionUniform generationNumber of nodesAverage propertiesRandom generationGraphNode degreeSuch networksSuch methodsAlgorithmNew methodNetworkDistributionTheoryInferenceConstraintsGuarantees
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