Loading…
Fitting sparse Markov models through a collapsed Gibbs sampler
Sparse Markov models (SMMs) provide a parsimonious representation for higher-order Markov models. We present a computationally efficient method for fitting SMMs using a collapsed Gibbs sampler, the GSDPMM. We prove the consistency of the GSDPMM in fitting SMMs. In simulations, the GSDPMM was found t...
Saved in:
Published in: | Computational statistics 2023-12, Vol.38 (4), p.1977-1994 |
---|---|
Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | cdi_FETCH-LOGICAL-c233t-3a06cbb15bf1d6ae0afd09447c1161421233d6eec921db523227745ae23d61b63 |
container_end_page | 1994 |
container_issue | 4 |
container_start_page | 1977 |
container_title | Computational statistics |
container_volume | 38 |
creator | Bennett, Iris Martin, Donald E. K. Lahiri, Soumendra Nath |
description | Sparse Markov models (SMMs) provide a parsimonious representation for higher-order Markov models. We present a computationally efficient method for fitting SMMs using a collapsed Gibbs sampler, the GSDPMM. We prove the consistency of the GSDPMM in fitting SMMs. In simulations, the GSDPMM was found to perform as well or better than existing methods for fitting SMMs. We apply the GSDPMM method to fit SMMs to patterns of wind speeds and DNA sequences. |
doi_str_mv | 10.1007/s00180-022-01310-8 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2889791530</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2889791530</sourcerecordid><originalsourceid>FETCH-LOGICAL-c233t-3a06cbb15bf1d6ae0afd09447c1161421233d6eec921db523227745ae23d61b63</originalsourceid><addsrcrecordid>eNp9kEFLxDAQhYMouK7-AU8Bz9GZpE3biyCLuworXvQc0jbtdu1uaqYr-O-NVvDmaWD43ps3j7FLhGsEyG4IAHMQIKUAVAgiP2Iz1KhEodP8mM2gSJRIQMtTdka0hUhmEmfsdtmNY7dvOQ02kONPNrz5D77zteuJj5vgD-2GW175vrcDuZqvurIkTnY39C6cs5PG9uQufuecvS7vXxYPYv28elzcrUUllRqFsqCrssS0bLDW1oFt6pgoySqMIROJkaq1c1UhsS5TqWK6LEmtk3GNpVZzdjX5DsG_HxyNZusPYR9PGpnnRVZgqiBScqKq4ImCa8wQup0NnwbBfPdkpp5M_N789GTyKFKTiCK8b134s_5H9QVs9mmY</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2889791530</pqid></control><display><type>article</type><title>Fitting sparse Markov models through a collapsed Gibbs sampler</title><source>ABI/INFORM Global</source><source>Springer Link</source><creator>Bennett, Iris ; Martin, Donald E. K. ; Lahiri, Soumendra Nath</creator><creatorcontrib>Bennett, Iris ; Martin, Donald E. K. ; Lahiri, Soumendra Nath</creatorcontrib><description>Sparse Markov models (SMMs) provide a parsimonious representation for higher-order Markov models. We present a computationally efficient method for fitting SMMs using a collapsed Gibbs sampler, the GSDPMM. We prove the consistency of the GSDPMM in fitting SMMs. In simulations, the GSDPMM was found to perform as well or better than existing methods for fitting SMMs. We apply the GSDPMM method to fit SMMs to patterns of wind speeds and DNA sequences.</description><identifier>ISSN: 0943-4062</identifier><identifier>EISSN: 1613-9658</identifier><identifier>DOI: 10.1007/s00180-022-01310-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Clustering ; Economic Theory/Quantitative Economics/Mathematical Methods ; Gene sequencing ; Markov analysis ; Markov chains ; Mathematics and Statistics ; Original Paper ; Probability ; Probability and Statistics in Computer Science ; Probability Theory and Stochastic Processes ; Samplers ; Sparsity ; Statistics</subject><ispartof>Computational statistics, 2023-12, Vol.38 (4), p.1977-1994</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c233t-3a06cbb15bf1d6ae0afd09447c1161421233d6eec921db523227745ae23d61b63</cites><orcidid>0000-0002-8475-1631</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2889791530/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2889791530?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,44363,74895</link.rule.ids></links><search><creatorcontrib>Bennett, Iris</creatorcontrib><creatorcontrib>Martin, Donald E. K.</creatorcontrib><creatorcontrib>Lahiri, Soumendra Nath</creatorcontrib><title>Fitting sparse Markov models through a collapsed Gibbs sampler</title><title>Computational statistics</title><addtitle>Comput Stat</addtitle><description>Sparse Markov models (SMMs) provide a parsimonious representation for higher-order Markov models. We present a computationally efficient method for fitting SMMs using a collapsed Gibbs sampler, the GSDPMM. We prove the consistency of the GSDPMM in fitting SMMs. In simulations, the GSDPMM was found to perform as well or better than existing methods for fitting SMMs. We apply the GSDPMM method to fit SMMs to patterns of wind speeds and DNA sequences.</description><subject>Algorithms</subject><subject>Clustering</subject><subject>Economic Theory/Quantitative Economics/Mathematical Methods</subject><subject>Gene sequencing</subject><subject>Markov analysis</subject><subject>Markov chains</subject><subject>Mathematics and Statistics</subject><subject>Original Paper</subject><subject>Probability</subject><subject>Probability and Statistics in Computer Science</subject><subject>Probability Theory and Stochastic Processes</subject><subject>Samplers</subject><subject>Sparsity</subject><subject>Statistics</subject><issn>0943-4062</issn><issn>1613-9658</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp9kEFLxDAQhYMouK7-AU8Bz9GZpE3biyCLuworXvQc0jbtdu1uaqYr-O-NVvDmaWD43ps3j7FLhGsEyG4IAHMQIKUAVAgiP2Iz1KhEodP8mM2gSJRIQMtTdka0hUhmEmfsdtmNY7dvOQ02kONPNrz5D77zteuJj5vgD-2GW175vrcDuZqvurIkTnY39C6cs5PG9uQufuecvS7vXxYPYv28elzcrUUllRqFsqCrssS0bLDW1oFt6pgoySqMIROJkaq1c1UhsS5TqWK6LEmtk3GNpVZzdjX5DsG_HxyNZusPYR9PGpnnRVZgqiBScqKq4ImCa8wQup0NnwbBfPdkpp5M_N789GTyKFKTiCK8b134s_5H9QVs9mmY</recordid><startdate>20231201</startdate><enddate>20231201</enddate><creator>Bennett, Iris</creator><creator>Martin, Donald E. K.</creator><creator>Lahiri, Soumendra Nath</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7TB</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AL</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>8G5</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>KR7</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2O</scope><scope>M2P</scope><scope>M7S</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-8475-1631</orcidid></search><sort><creationdate>20231201</creationdate><title>Fitting sparse Markov models through a collapsed Gibbs sampler</title><author>Bennett, Iris ; Martin, Donald E. K. ; Lahiri, Soumendra Nath</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c233t-3a06cbb15bf1d6ae0afd09447c1161421233d6eec921db523227745ae23d61b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Clustering</topic><topic>Economic Theory/Quantitative Economics/Mathematical Methods</topic><topic>Gene sequencing</topic><topic>Markov analysis</topic><topic>Markov chains</topic><topic>Mathematics and Statistics</topic><topic>Original Paper</topic><topic>Probability</topic><topic>Probability and Statistics in Computer Science</topic><topic>Probability Theory and Stochastic Processes</topic><topic>Samplers</topic><topic>Sparsity</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bennett, Iris</creatorcontrib><creatorcontrib>Martin, Donald E. K.</creatorcontrib><creatorcontrib>Lahiri, Soumendra Nath</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Research Library (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer science database</collection><collection>Civil Engineering Abstracts</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>ProQuest research library</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>Research Library (Corporate)</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>ProQuest Central Basic</collection><jtitle>Computational statistics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bennett, Iris</au><au>Martin, Donald E. K.</au><au>Lahiri, Soumendra Nath</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fitting sparse Markov models through a collapsed Gibbs sampler</atitle><jtitle>Computational statistics</jtitle><stitle>Comput Stat</stitle><date>2023-12-01</date><risdate>2023</risdate><volume>38</volume><issue>4</issue><spage>1977</spage><epage>1994</epage><pages>1977-1994</pages><issn>0943-4062</issn><eissn>1613-9658</eissn><abstract>Sparse Markov models (SMMs) provide a parsimonious representation for higher-order Markov models. We present a computationally efficient method for fitting SMMs using a collapsed Gibbs sampler, the GSDPMM. We prove the consistency of the GSDPMM in fitting SMMs. In simulations, the GSDPMM was found to perform as well or better than existing methods for fitting SMMs. We apply the GSDPMM method to fit SMMs to patterns of wind speeds and DNA sequences.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00180-022-01310-8</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0002-8475-1631</orcidid></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0943-4062 |
ispartof | Computational statistics, 2023-12, Vol.38 (4), p.1977-1994 |
issn | 0943-4062 1613-9658 |
language | eng |
recordid | cdi_proquest_journals_2889791530 |
source | ABI/INFORM Global; Springer Link |
subjects | Algorithms Clustering Economic Theory/Quantitative Economics/Mathematical Methods Gene sequencing Markov analysis Markov chains Mathematics and Statistics Original Paper Probability Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Samplers Sparsity Statistics |
title | Fitting sparse Markov models through a collapsed Gibbs sampler |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T16%3A45%3A43IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Fitting%20sparse%20Markov%20models%20through%20a%20collapsed%20Gibbs%20sampler&rft.jtitle=Computational%20statistics&rft.au=Bennett,%20Iris&rft.date=2023-12-01&rft.volume=38&rft.issue=4&rft.spage=1977&rft.epage=1994&rft.pages=1977-1994&rft.issn=0943-4062&rft.eissn=1613-9658&rft_id=info:doi/10.1007/s00180-022-01310-8&rft_dat=%3Cproquest_cross%3E2889791530%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c233t-3a06cbb15bf1d6ae0afd09447c1161421233d6eec921db523227745ae23d61b63%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2889791530&rft_id=info:pmid/&rfr_iscdi=true |