{"id":"https://openalex.org/W199719331","doi":"https://doi.org/10.48550/arxiv.1309.6870","title":"Dynamic Blocking and Collapsing for Gibbs Sampling","display_name":"Dynamic Blocking and Collapsing for Gibbs Sampling","publication_year":2013,"publication_date":"2013-09-26","ids":{"openalex":"https://openalex.org/W199719331","doi":"https://doi.org/10.48550/arxiv.1309.6870","mag":"199719331"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:1309.6870","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1309.6870","pdf_url":"https://arxiv.org/pdf/1309.6870","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1309.6870","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103421781","display_name":"Deepak Venugopal","orcid":"https://orcid.org/0000-0001-7466-5417","observed_orcids":["https://orcid.org/0000-0001-7466-5417"]},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Venugopal, Deepak","raw_affiliation_strings":["Dept. of Computer Science, The University of Texas at Dallas, Richardson, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, The University of Texas at Dallas, Richardson, TX","institution_ids":["https://openalex.org/I162577319"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5038455119","display_name":"Vibhav Gogate","orcid":"https://orcid.org/0000-0002-6459-7358","observed_orcids":["https://orcid.org/0000-0002-6459-7358"]},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gogate, Vibhav","raw_affiliation_strings":["Dept. of Computer Science, The University of Texas at Dallas, Richardson, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, The University of Texas at Dallas, Richardson, TX","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162577319"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"664","last_page":"673"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.8618000149726868,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.8618000149726868,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.13490000367164612,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0005000000237487257,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.9810000061988831},{"id":"https://openalex.org/keywords/blocking","display_name":"blocking","score":0.7760000228881836},{"id":"https://openalex.org/keywords/variable-elimination","display_name":"variable elimination","score":0.8190000057220459},{"id":"https://openalex.org/keywords/probabilistic-graphical-models","display_name":"probabilistic graphical models","score":0.9879999756813049},{"id":"https://openalex.org/keywords/multi-objective-optimization","display_name":"multi-objective optimization","score":0.9049999713897705},{"id":"https://openalex.org/keywords/greedy-algorithm","display_name":"greedy algorithm","score":0.6330000162124634},{"id":"https://openalex.org/keywords/variable-partitioning","display_name":"variable partitioning","score":0.7429999709129333},{"id":"https://openalex.org/keywords/correlation-statistics","display_name":"correlation statistics","score":0.7639999985694885},{"id":"https://openalex.org/keywords/markov-chain-mixing","display_name":"Markov chain mixing","score":0.6850000023841858},{"id":"https://openalex.org/keywords/sampling-accuracy","display_name":"sampling accuracy","score":0.3919999897480011}],"concepts":[{"id":"https://openalex.org/C144745244","wikidata":"https://www.wikidata.org/wiki/Q4927286","display_name":"Blocking (statistics)","level":2,"score":0.8173248767852783},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7011016607284546},{"id":"https://openalex.org/C155846161","wikidata":"https://www.wikidata.org/wiki/Q1143367","display_name":"Graphical model","level":2,"score":0.5772479772567749},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.5221348404884338},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5077627897262573},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5022664070129395},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4987473487854004},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.49201199412345886},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.4913412630558014},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4546509087085724},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4391306936740875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.24078857898712158},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.226748526096344},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.22312650084495544},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.22263559699058533},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:arXiv.org:1309.6870","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1309.6870","pdf_url":"https://arxiv.org/pdf/1309.6870","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.369.9299","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.369.9299","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.hlt.utdallas.edu/~vgogate/papers/uai13-2.pdf","raw_type":"text"},{"id":"doi:10.48550/arxiv.1309.6870","doi":"https://doi.org/10.48550/arxiv.1309.6870","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1309.6870","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"},{"id":"mag:199719331","doi":null,"is_oa":false,"landing_page_url":"http://auai.org/uai2013/prints/papers/221.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306421103","display_name":"Uncertainty in Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":"Uncertainty in Artificial Intelligence","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1309.6870","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/1309.6870","pdf_url":"https://arxiv.org/pdf/1309.6870","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W314780631","https://openalex.org/W1496450597","https://openalex.org/W1511986666","https://openalex.org/W1535430927","https://openalex.org/W1543403517","https://openalex.org/W1593793857","https://openalex.org/W1860880244","https://openalex.org/W1977970897","https://openalex.org/W1982333717","https://openalex.org/W1997188340","https://openalex.org/W1997945384","https://openalex.org/W2020999234","https://openalex.org/W2069739265","https://openalex.org/W2085550081","https://openalex.org/W2090361527","https://openalex.org/W2133933452","https://openalex.org/W2154055561","https://openalex.org/W2161202741","https://openalex.org/W2162898443","https://openalex.org/W2164568295","https://openalex.org/W2179629101","https://openalex.org/W2293897496","https://openalex.org/W2395355800","https://openalex.org/W3099686279"],"related_works":["https://openalex.org/W2949120782","https://openalex.org/W2952212725","https://openalex.org/W2115468995","https://openalex.org/W2140519248","https://openalex.org/W2739672424","https://openalex.org/W2896042778","https://openalex.org/W2982570259","https://openalex.org/W1618644968","https://openalex.org/W2468462628","https://openalex.org/W2963062669","https://openalex.org/W2944971886","https://openalex.org/W2187098660","https://openalex.org/W2748147629","https://openalex.org/W2808679659","https://openalex.org/W3034713226","https://openalex.org/W2913554033","https://openalex.org/W3082340587","https://openalex.org/W3161115020","https://openalex.org/W3006403619","https://openalex.org/W2902709898"],"abstract_inverted_index":{"In":[0],"this":[1,53,66,116],"paper,":[2],"we":[3,69],"investigate":[4],"combining":[5,33],"blocking":[6,77,146],"and":[7,49,78,80,93,129,141,148],"collapsing":[8,39,149],"--":[9,21],"two":[10,71],"widely":[11],"used":[12],"strategies":[13],"for":[14,64,76,113],"improving":[15],"the":[16,23,47,82,86,89,125,138,156],"accuracy":[17],"of":[18,25,84,159,179,183],"Gibbs":[19],"sampling":[20],"in":[22,46,50,88,181],"context":[24],"probabilistic":[26],"graphical":[27],"models":[28],"(PGMs).":[29],"We":[30,58,107,153],"show":[31],"that":[32],"them":[34],"is":[35],"not":[36],"straight-forward":[37],"because":[38],"(or":[40],"eliminating":[41],"variables)":[42],"introduces":[43],"new":[44],"dependencies":[45],"PGM":[48,90],"computation-limited":[51],"settings,":[52],"may":[54],"adversely":[55],"affect":[56],"blocking.":[57],"therefore":[59],"propose":[60,108],"a":[61,103,109],"principled":[62],"approach":[63,170],"tackling":[65],"problem.":[67,119],"Specifically,":[68],"develop":[70],"scoring":[72,100],"functions,":[73],"one":[74],"each":[75],"collapsing,":[79],"formulate":[81],"problem":[83],"partitioning":[85,126],"variables":[87,131],"into":[91,127],"blocked":[92,128],"collapsed":[94,130],"subsets":[95],"as":[96,163],"simultaneously":[97],"maximizing":[98],"both":[99],"functions":[101],"(i.e.,":[102],"multi-objective":[104],"optimization":[105,118],"problem).":[106],"dynamic,":[110],"greedy":[111],"algorithm":[112,122],"approximately":[114],"solving":[115],"intractable":[117],"Our":[120],"dynamic":[121,161,169],"periodically":[123],"updates":[124],"by":[132,145,176],"leveraging":[133],"correlation":[134],"statistics":[135],"gathered":[136],"from":[137],"generated":[139],"samples":[140,165],"enables":[142],"rapid":[143],"mixing":[144],"together":[147],"highly":[150],"correlated":[151],"variables.":[152],"demonstrate":[154],"experimentally":[155],"clear":[157],"benefit":[158],"our":[160,168],"approach:":[162],"more":[164],"are":[166],"drawn,":[167],"significantly":[171],"outperforms":[172],"static":[173],"graph-based":[174],"approaches":[175],"an":[177],"order":[178],"magnitude":[180],"terms":[182],"accuracy.":[184]},"counts_by_year":[{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2025-10-10T00:00:00"}