Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods.
<p>HMFGraph Gibbs, G-Wishart, and BGGM were run with 5000 iterations. For all <i>p</i>, .</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852015327045484544 |
|---|---|
| author | Aapo E. Korhonen (22530674) |
| author2 | Olli Sarala (22530677) Tuomas Hautamäki (22530680) Markku Kuismin (2170918) Mikko J. Sillanpää (8139792) |
| author2_role | author author author author |
| author_facet | Aapo E. Korhonen (22530674) Olli Sarala (22530677) Tuomas Hautamäki (22530680) Markku Kuismin (2170918) Mikko J. Sillanpää (8139792) |
| author_role | author |
| dc.creator.none.fl_str_mv | Aapo E. Korhonen (22530674) Olli Sarala (22530677) Tuomas Hautamäki (22530680) Markku Kuismin (2170918) Mikko J. Sillanpää (8139792) |
| dc.date.none.fl_str_mv | 2025-10-30T17:49:42Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pcbi.1013614.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Comparisons_of_median_computation_times_in_seconds_based_on_5_runs_for_the_GEM_algorithm_and_the_MCMC_methods_/30493907 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Genetics Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified recovering meaningful networks partial correlation networks false discovery rate explain complex relationships novel bayesian approach dimensional omics datasets biological example analyses estimated precision matrix large datasets hierarchical matrix estimated f biological variables bayesian implementations whose width specific choice recently received r package powerful tools new way maximization algorithm good properties generalized expectation fast implementation extensive simulations edge selection demanding parts condition number community detection art approaches |
| dc.title.none.fl_str_mv | Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>HMFGraph Gibbs, G-Wishart, and BGGM were run with 5000 iterations. For all <i>p</i>, .</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_3083c8345cc0c24f17cef9addd03b607 |
| identifier_str_mv | 10.1371/journal.pcbi.1013614.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30493907 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods.Aapo E. Korhonen (22530674)Olli Sarala (22530677)Tuomas Hautamäki (22530680)Markku Kuismin (2170918)Mikko J. Sillanpää (8139792)GeneticsBiotechnologyCancerBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedrecovering meaningful networkspartial correlation networksfalse discovery rateexplain complex relationshipsnovel bayesian approachdimensional omics datasetsbiological example analysesestimated precision matrixlarge datasetshierarchical matrixestimated fbiological variablesbayesian implementationswhose widthspecific choicerecently receivedr packagepowerful toolsnew waymaximization algorithmgood propertiesgeneralized expectationfast implementationextensive simulationsedge selectiondemanding partscondition numbercommunity detectionart approaches<p>HMFGraph Gibbs, G-Wishart, and BGGM were run with 5000 iterations. For all <i>p</i>, .</p>2025-10-30T17:49:42ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pcbi.1013614.t002https://figshare.com/articles/dataset/Comparisons_of_median_computation_times_in_seconds_based_on_5_runs_for_the_GEM_algorithm_and_the_MCMC_methods_/30493907CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304939072025-10-30T17:49:42Z |
| spellingShingle | Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods. Aapo E. Korhonen (22530674) Genetics Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified recovering meaningful networks partial correlation networks false discovery rate explain complex relationships novel bayesian approach dimensional omics datasets biological example analyses estimated precision matrix large datasets hierarchical matrix estimated f biological variables bayesian implementations whose width specific choice recently received r package powerful tools new way maximization algorithm good properties generalized expectation fast implementation extensive simulations edge selection demanding parts condition number community detection art approaches |
| status_str | publishedVersion |
| title | Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods. |
| title_full | Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods. |
| title_fullStr | Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods. |
| title_full_unstemmed | Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods. |
| title_short | Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods. |
| title_sort | Comparisons of median computation times (in seconds) based on 5 runs for the GEM algorithm and the MCMC methods. |
| topic | Genetics Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified recovering meaningful networks partial correlation networks false discovery rate explain complex relationships novel bayesian approach dimensional omics datasets biological example analyses estimated precision matrix large datasets hierarchical matrix estimated f biological variables bayesian implementations whose width specific choice recently received r package powerful tools new way maximization algorithm good properties generalized expectation fast implementation extensive simulations edge selection demanding parts condition number community detection art approaches |