Search results for "Multinomial model"

showing 2 items of 2 documents

Empirical Bayes improves assessments of diversity and similarity when overdispersion prevails in taxonomic counts with no covariates

2019

Abstract The assessment of diversity and similarity is relevant in monitoring the status of ecosystems. The respective indicators are based on the taxonomic composition of biological communities of interest, currently estimated through the proportions computed from sampling multivariate counts. In this work we present a novel method to estimate the taxonomic composition able to work even with a single sample and no covariates, when data are affected by overdispersion. The presence of overdispersion in taxonomic counts may be the result of significant environmental factors which are often unobservable but influence communities. Following the empirical Bayes approach, we combine a Bayesian mo…

0106 biological sciencesMultivariate statisticsBiological dataEmpirical Bayesian estimationEcologyTaxonomic compositionGeneral Decision SciencesEnvironmental monitoring010501 environmental sciencesBayesian inference010603 evolutionary biology01 natural sciencesBiodiversity assessment; Dirichlet-Multinomial model; Empirical Bayesian estimation; Environmental monitoring; Taxonomic compositionMarginal likelihoodBayes' theoremOverdispersionStatisticsTaxonomic rankDirichlet-Multinomial modelBiodiversity assessmentEcology Evolution Behavior and Systematics0105 earth and related environmental sciencesEmpirical Bayes methodMathematics
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Overall Objective Priors

2015

In multi-parameter models, reference priors typically depend on the parameter or quantity of interest, and it is well known that this is necessary to produce objective posterior distributions with optimal properties. There are, however, many situations where one is simultaneously interested in all the parameters of the model or, more realistically, in functions of them that include aspects such as prediction, and it would then be useful to have a single objective prior that could safely be used to produce reasonable posterior inferences for all the quantities of interest. In this paper, we consider three methods for selecting a single objective prior and study, in a variety of problems incl…

Statistics and ProbabilityComputer sciencebusiness.industryApplied MathematicsMathematics - Statistics TheoryStatistics Theory (math.ST)Joint Reference PriorReference AnalysisMachine learningcomputer.software_genreLogarithmic DivergenceObjective PriorsVariety (cybernetics)Single objectiveMultinomial ModelPrior probabilityFOS: MathematicsMultinomial distributionMultinomial modelArtificial intelligencebusinesscomputerReference analysisBayesian Analysis
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