Search results for "probability"

showing 10 items of 3417 documents

Tamaño optimo de una muestra: Solucion Bayesiana

1975

Statistics and ProbabilityComputer scienceStatistics Probability and UncertaintyTrabajos de Estadistica y de Investigacion Operativa
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Correction: Correcting for non-ignorable missingness in smoking trends

2017

Statistics and ProbabilityComputer scienceStatisticsStatistics Probability and UncertaintyMissing dataStat
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Preface: Special Issue on Structure in Glassy and Jammed Systems

2016

This special issue presents new developments in our understanding of the role of structure in dynamical arrest and jamming. Articles highlight local geometric motifs and other forms of amorphous order, in experiment, computer simulation and theory.

Statistics and ProbabilityComputer scienceStructure (category theory)Statistical and Nonlinear PhysicsJamming02 engineering and technology021001 nanoscience & nanotechnology01 natural sciencesAmorphous solidOrder (business)0103 physical sciencesStatistical physicsStatistics Probability and Uncertainty010306 general physics0210 nano-technologyJournal of Statistical Mechanics: Theory and Experiment
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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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Sequential Monte Carlo methods in Bayesian joint models for longitudinal and time-to-event data

2020

The statistical analysis of the information generated by medical follow-up is a very important challenge in the field of personalized medicine. As the evolutionary course of a patient's disease progresses, his/her medical follow-up generates more and more information that should be processed immediately in order to review and update his/her prognosis and treatment. Hence, we focus on this update process through sequential inference methods for joint models of longitudinal and time-to-event data from a Bayesian perspective. More specifically, we propose the use of sequential Monte Carlo (SMC) methods for static parameter joint models with the intention of reducing computational time in each…

Statistics and ProbabilityComputer sciencebusiness.industryBayesian probabilitySequential monte carlo methodsMachine learningcomputer.software_genre01 natural sciencesField (computer science)010104 statistics & probability03 medical and health sciences0302 clinical medicineEvent data030220 oncology & carcinogenesisStatistical analysisPersonalized medicineArtificial intelligence0101 mathematicsStatistics Probability and UncertaintybusinessJoint (audio engineering)CartographycomputerStatistical Modelling
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A review of second‐order blind identification methods

2021

Second-order source separation (SOS) is a data analysis tool which can be used for revealing hidden structures in multivariate time series data or as a tool for dimension reduction. Such methods are nowadays increasingly important as more and more high-dimensional multivariate time series data are measured in numerous fields of applied science. Dimension reduction is crucial, as modeling such high-dimensional data with multivariate time series models is often impractical as the number of parameters describing dependencies between the component time series is usually too high. SOS methods have their roots in the signal processing literature, where they were first used to separate source sign…

Statistics and ProbabilityComputer sciencebusiness.industryDimensionality reductionSecond order blind identificationPattern recognitionArtificial intelligencebusinessBlind signal separationWIREs Computational Statistics
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DySC: software for greedy clustering of 16S rRNA reads.

2012

Abstract Summary: Pyrosequencing technologies are frequently used for sequencing the 16S ribosomal RNA marker gene for profiling microbial communities. Clustering of the produced reads is an important but time-consuming task. We present Dynamic Seed-based Clustering (DySC), a new tool based on the greedy clustering approach that uses a dynamic seeding strategy. Evaluations based on the normalized mutual information (NMI) criterion show that DySC produces higher quality clusters than UCLUST and CD-HIT at a comparable runtime. Availability and implementation: DySC, implemented in C, is available at http://code.google.com/p/dysc/ under GNU GPL license. Contact:  bertil.schmidt@uni-mainz.de Sup…

Statistics and ProbabilityComputer sciencebusiness.industrySequence Analysis RNA16S ribosomal RNAcomputer.software_genreBiochemistryComputer Science ApplicationsComputational MathematicsSoftwareComputational Theory and MathematicsRNA Ribosomal 16SCluster AnalysisMetagenomeData miningCluster analysisbusinessMolecular BiologycomputerSoftwareBioinformatics (Oxford, England)
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Musket: a multistage k-mer spectrum-based error corrector for Illumina sequence data

2012

Abstract Motivation: The imperfect sequence data produced by next-generation sequencing technologies have motivated the development of a number of short-read error correctors in recent years. The majority of methods focus on the correction of substitution errors, which are the dominant error source in data produced by Illumina sequencing technology. Existing tools either score high in terms of recall or precision but not consistently high in terms of both measures. Results: In this article, we present Musket, an efficient multistage k-mer-based corrector for Illumina short-read data. We use the k-mer spectrum approach and introduce three correction techniques in a multistage workflow: two-s…

Statistics and ProbabilityComputer sciencebusiness.industrySequence assemblySequence Analysis DNAMusketBiochemistryComputer Science ApplicationsComputational MathematicsCUDASoftwareComputational Theory and Mathematicsk-merEscherichia coliChromosomes HumanHumansbusinessFocus (optics)Molecular BiologyAlgorithmAlgorithmsGenome BacterialSoftwareIllumina dye sequencingBioinformatics
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Recurrence Plots in Nonlinear Time Series Analysis: Free Software

2002

Recurrence plots are graphical devices specially suited to detect hidden dynamical patterns and nonlinearities in data. However, there are few programs available to apply such a mehodology. This paper reviews one of the best free programs to apply nonlinear time series analysis: Visual Recurrence Analysis (VRA). This program is targeted to recurrence analysis and the so-called Recurrence Quantitative Analysis (RQA, the quantitative counterpart of recurrence plots), although it includes many procedures in a friendly visual environment. Comparisons with alternative programs are performed.

Statistics and ProbabilityComputer sciencebusiness.industrycomputer.software_genreNonlinear time series analysisSoftwareQuantitative analysis (finance)StatisticsData miningStatistics Probability and Uncertaintybusinesslcsh:Statisticslcsh:HA1-4737computerSoftwareJournal of Statistical Software
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Extending graphical models for applications: on covariates, missingness and normality

2021

The authors of the paper “Bayesian Graphical Models for Modern Biological Applications” have put forward an important framework for making graphical models more useful in applied settings. In this discussion paper, we give a number of suggestions for making this framework even more suitable for practical scenarios. Firstly, we show that an alternative and simplified definition of covariate might make the framework more manageable in high-dimensional settings. Secondly, we point out that the inclusion of missing variables is important for practical data analysis. Finally, we comment on the effect that the Gaussianity assumption has in identifying the underlying conditional independence graph…

Statistics and ProbabilityComputer sciencemedia_common.quotation_subjectMissing dataConditional graphical modelsCopula graphical modelsMissing dataCovariateEconometricsSparse inferenceGraphical modelStatistics Probability and UncertaintyNormalitymedia_common
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