{"id":"https://openalex.org/W4289637376","doi":"https://doi.org/10.1515/mcma-2022-2119","title":"Approximate bounding of mixing time for multiple-step Gibbs samplers","display_name":"Approximate bounding of mixing time for multiple-step Gibbs samplers","publication_year":2022,"publication_date":"2022-08-03","ids":{"openalex":"https://openalex.org/W4289637376","doi":"https://doi.org/10.1515/mcma-2022-2119"},"language":"en","primary_location":{"id":"doi:10.1515/mcma-2022-2119","is_oa":false,"landing_page_url":"https://doi.org/10.1515/mcma-2022-2119","pdf_url":null,"source":{"id":"https://openalex.org/S142132501","display_name":"Monte Carlo Methods and Applications","issn_l":"0929-9629","issn":["0929-9629","1569-3961"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Monte Carlo Methods and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5080752072","display_name":"David A. Spade","orcid":"https://orcid.org/0000-0001-6326-8635"},"institutions":[{"id":"https://openalex.org/I43579087","display_name":"University of Wisconsin\u2013Milwaukee","ror":"https://ror.org/031q21x57","country_code":"US","type":"education","lineage":["https://openalex.org/I43579087"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"David Spade","raw_affiliation_strings":["University of Wisconsin\u2013Milwaukee , Milwaukee , WI , USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Wisconsin\u2013Milwaukee , Milwaukee , WI , USA","institution_ids":["https://openalex.org/I43579087"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5080752072"],"corresponding_institution_ids":["https://openalex.org/I43579087"],"apc_list":null,"apc_paid":null,"fwci":0.2452,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.57595731,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"28","issue":"3","first_page":"221","last_page":"233"},"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":0.9986000061035156,"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":0.9986000061035156,"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/T10136","display_name":"Statistical Methods and Inference","score":0.9979000091552734,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9979000091552734,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8990000486373901},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.7302752137184143},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5910360217094421},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.5607215762138367},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.5318909287452698},{"id":"https://openalex.org/keywords/metropolis\u2013hastings-algorithm","display_name":"Metropolis\u2013Hastings algorithm","score":0.505169689655304},{"id":"https://openalex.org/keywords/rejection-sampling","display_name":"Rejection sampling","score":0.49551355838775635},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.47126269340515137},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.45632246136665344},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4547102749347687},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.4399600028991699},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.431735098361969},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4227936863899231},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.3921129107475281},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.38495326042175293},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.31452345848083496},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.11768931150436401},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.10426878929138184},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.09580191969871521}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8990000486373901},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.7302752137184143},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5910360217094421},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.5607215762138367},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5318909287452698},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.505169689655304},{"id":"https://openalex.org/C187192777","wikidata":"https://www.wikidata.org/wiki/Q381699","display_name":"Rejection sampling","level":5,"score":0.49551355838775635},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.47126269340515137},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.45632246136665344},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4547102749347687},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.4399600028991699},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.431735098361969},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4227936863899231},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.3921129107475281},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.38495326042175293},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.31452345848083496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.11768931150436401},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.10426878929138184},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.09580191969871521},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1515/mcma-2022-2119","is_oa":false,"landing_page_url":"https://doi.org/10.1515/mcma-2022-2119","pdf_url":null,"source":{"id":"https://openalex.org/S142132501","display_name":"Monte Carlo Methods and Applications","issn_l":"0929-9629","issn":["0929-9629","1569-3961"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310313990","host_organization_name":"De Gruyter","host_organization_lineage":["https://openalex.org/P4310313990"],"host_organization_lineage_names":["De Gruyter"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Monte Carlo Methods and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.49000000953674316,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W156498718","https://openalex.org/W767349109","https://openalex.org/W1520063096","https://openalex.org/W1628017834","https://openalex.org/W1660492679","https://openalex.org/W1966862482","https://openalex.org/W2001855864","https://openalex.org/W2017874618","https://openalex.org/W2047000577","https://openalex.org/W2049488596","https://openalex.org/W2055218217","https://openalex.org/W2077039755","https://openalex.org/W2095763317","https://openalex.org/W2102650396","https://openalex.org/W2128266748","https://openalex.org/W2129631522","https://openalex.org/W2136521556","https://openalex.org/W2146764432","https://openalex.org/W2148534890","https://openalex.org/W3104123323","https://openalex.org/W3122858766","https://openalex.org/W3187933891","https://openalex.org/W4388289831","https://openalex.org/W4388289850"],"related_works":["https://openalex.org/W2539839227","https://openalex.org/W4295750535","https://openalex.org/W3097509027","https://openalex.org/W2045761178","https://openalex.org/W105676162","https://openalex.org/W2914481316","https://openalex.org/W1539868720","https://openalex.org/W4294529948","https://openalex.org/W3198973722","https://openalex.org/W2162021827"],"abstract_inverted_index":{"Abstract":[0],"Markov":[1],"chain":[2,52,100],"Monte":[3,128],"Carlo":[4,129],"(MCMC)":[5],"methods":[6],"are":[7],"important":[8,40],"in":[9,67,104],"a":[10,96,105,138],"variety":[11],"of":[12,45,70,98,115,151],"statistical":[13],"applications":[14],"that":[15,81],"require":[16],"sampling":[17,126],"from":[18,86],"intractable":[19],"probability":[20,89],"distributions.":[21],"Among":[22],"the":[23,29,51,68,71,74,87,99,113,148,152],"most":[24],"common":[25],"MCMC":[26,34],"algorithms":[27],"is":[28,36,39,64],"Gibbs":[30,125,155],"sampler.":[31,156],"When":[32],"an":[33,43,82,143],"algorithm":[35],"used,":[37],"it":[38,48],"to":[41,53,56,77,141],"have":[42],"idea":[44],"how":[46],"long":[47],"takes":[49],"for":[50,124],"become":[54],"\u201cclose\u201d":[55],"its":[57],"stationary":[58],"distribution.":[59],"In":[60],"many":[61],"cases,":[62],"there":[63],"high":[65],"autocorrelation":[66],"output":[69,75],"chain,":[72],"so":[73,80],"needs":[76],"be":[78,92],"thinned":[79],"approximate":[83,144],"random":[84],"sample":[85],"desired":[88],"distribution":[90],"can":[91],"obtained":[93],"by":[94,136],"taking":[95],"state":[97],"every":[101],"h":[102,108,153],"steps":[103],"process":[106],"called":[107],"-thinning.":[109],"This":[110],"manuscript":[111],"extends":[112],"work":[114],"[D.":[116],"A.":[117],"Spade,":[118],"Estimating":[119],"drift":[120],"and":[121],"minorization":[122],"coefficients":[123],"algorithms,":[127],"Methods":[130],"Appl.":[131],"27":[132],"2021,":[133],"3,":[134],"195\u2013209]":[135],"presenting":[137],"computational":[139],"approach":[140],"obtaining":[142],"upper":[145],"bound":[146],"on":[147],"mixing":[149],"time":[150],"-thinned":[154]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
