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Stochastic Version of EM Algorithm for Nonlinear Random ChangePoint Models

Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...)(2021)10.11159/icsta21.119Source: DataRank Database

Stochastic Version of EM Algorithm for Nonlinear Random ChangePoint Models is a research paper published in Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...) (2021). On theSindex it has a DataRank of 0.

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (1 grants)Impact (FWCI 0.00)OA: bronzeTopics (25)IDs (PubMed, PMC, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (2)

Binod Manandhar ORCID,Hongbin Zhang ORCID

Live enrichment

PubMed:38919945 PMC:PMC11198015 OpenAlex:https://openalex.org/W3195182518

NIAID NIH HHS

Grant: R21 AI147933

FWCI

0.00

Citation Percentile

0.2%

bronze

conferencehttps://doi.org/10.11159/icsta21.119

publisherhttps://doi.org/10.11159/icsta21.119

conferencehttp://doi.org/10.11159/icsta21.119

repositoryhttps://pubmed.ncbi.nlm.nih.gov/38919945

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/11198015

Fields of Study

Statistical Methods and Bayesian InferenceBayesian Methods and Mixture ModelsStatistical Methods and Inference

Keywords

Gibbs samplingNonlinear systemPoint processExpectation–maximization algorithmComputer scienceInferenceAlgorithmStochastic processRandom effects modelMultivariate statisticsMathematical optimizationArtificial intelligenceMathematicsMachine learningStatisticsMaximum likelihoodBayesian probabilityNonlinear Mixed Effects ModelGibbs’s SamplerMultivariate Rejection SamplingRandom Change-point ModelStochastic Version Of Em