{"id":"https://openalex.org/W7100030004","doi":null,"title":"Markov chain Monte Carlo methods for statistical inference","display_name":"Markov chain Monte Carlo methods for statistical inference","publication_year":2004,"publication_date":"2004-01-01","ids":{"openalex":"https://openalex.org/W7100030004"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.320.9631","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.320.9631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ims.nus.edu.sg/Programs/mcmc/files/besag_tl.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5056693273","display_name":"Julian E. Besag","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Julian Besag","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5056693273"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.55006932,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":1.0,"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":1.0,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.0,"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/T10591","display_name":"Theoretical and Computational Physics","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3104","display_name":"Condensed Matter Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"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":0.9980000257492065},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.8429999947547913},{"id":"https://openalex.org/keywords/frequentist-inference","display_name":"frequentist inference","score":0.9760000109672546},{"id":"https://openalex.org/keywords/monte-carlo-methods","display_name":"Monte Carlo methods","score":0.8159999847412109},{"id":"https://openalex.org/keywords/metropolis-algorithm","display_name":"Metropolis algorithm","score":0.9610000252723694},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.9869999885559082},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"slice sampling","score":0.8209999799728394},{"id":"https://openalex.org/keywords/goodness-of-fit-tests","display_name":"goodness-of-fit tests","score":0.8610000014305115},{"id":"https://openalex.org/keywords/maximum-likelihood-estimation","display_name":"maximum likelihood estimation","score":0.9929999709129333},{"id":"https://openalex.org/keywords/auxiliary-variables","display_name":"auxiliary variables","score":0.8429999947547913},{"id":"https://openalex.org/keywords/perfect-sampling","display_name":"perfect sampling","score":0.9419999718666077},{"id":"https://openalex.org/keywords/coupling-from-the-past","display_name":"coupling from the past","score":0.9929999709129333},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"simulated annealing","score":0.8489999771118164},{"id":"https://openalex.org/keywords/statistical-computing","display_name":"statistical computing","score":0.5410000085830688}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8324999809265137},{"id":"https://openalex.org/C162376815","wikidata":"https://www.wikidata.org/wiki/Q2158281","display_name":"Frequentist inference","level":4,"score":0.7019000053405762},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.6233999729156494},{"id":"https://openalex.org/C204693719","wikidata":"https://www.wikidata.org/wiki/Q910810","display_name":"Metropolis\u2013Hastings algorithm","level":4,"score":0.6201000213623047},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.565500020980835},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.5602999925613403},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5216000080108643},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.51910001039505},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.504800021648407},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49810001254081726},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.4794999957084656},{"id":"https://openalex.org/C97074811","wikidata":"https://www.wikidata.org/wiki/Q6771322","display_name":"Markov chain mixing time","level":5,"score":0.46709999442100525},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41929998993873596},{"id":"https://openalex.org/C187192777","wikidata":"https://www.wikidata.org/wiki/Q381699","display_name":"Rejection sampling","level":5,"score":0.41029998660087585},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4002000093460083},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3939000070095062},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.3862000107765198},{"id":"https://openalex.org/C204493344","wikidata":"https://www.wikidata.org/wiki/Q6904698","display_name":"Monte Carlo method in statistical physics","level":5,"score":0.3725000023841858},{"id":"https://openalex.org/C132725507","wikidata":"https://www.wikidata.org/wiki/Q39879","display_name":"Monte Carlo integration","level":5,"score":0.3653999865055084},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.359499990940094},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.35260000824928284},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.351500004529953},{"id":"https://openalex.org/C189973286","wikidata":"https://www.wikidata.org/wiki/Q176695","display_name":"Markov property","level":4,"score":0.3416999876499176},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.3337000012397766},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3321000039577484},{"id":"https://openalex.org/C37669827","wikidata":"https://www.wikidata.org/wiki/Q6904703","display_name":"Monte Carlo molecular modeling","level":4,"score":0.328000009059906},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.30079999566078186},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.28760001063346863},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.26589998602867126},{"id":"https://openalex.org/C187653413","wikidata":"https://www.wikidata.org/wiki/Q7135015","display_name":"Parallel tempering","level":5,"score":0.2646999955177307},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.2563000023365021}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.320.9631","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.320.9631","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ims.nus.edu.sg/Programs/mcmc/files/besag_tl.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W1867735041","https://openalex.org/W1619437807","https://openalex.org/W2810989943","https://openalex.org/W7039193880","https://openalex.org/W2294136611","https://openalex.org/W2185375362","https://openalex.org/W1483015670","https://openalex.org/W1605906029","https://openalex.org/W2258778752","https://openalex.org/W2291288244"],"abstract_inverted_index":{"These":[0],"notes":[1],"provide":[2],"an":[3],"introduction":[4],"to":[5,14,89],"Markov":[6,51,64],"chain":[7,52,65],"Monte":[8,41,66],"Carlo":[9,42],"methods":[10,22,120],"and":[11,17,73,79,86,127],"their":[12],"applications":[13,131],"both":[15,75],"Bayesian":[16,33],"frequentist":[18],"statistical":[19],"inference.":[20],"Such":[21],"have":[23,45],"revolutionized":[24],"what":[25],"can":[26,55],"be":[27,58],"achieved":[28],"computationally,":[29],"especially":[30],"in":[31],"the":[32,46,50,70,76,80,105,116,135],"paradigm.":[34],"The":[35],"account":[36],"begins":[37],"by":[38],"discussing":[39],"ordinary":[40],"methods:":[43],"these":[44],"same":[47],"goals":[48],"as":[49,83],"versions":[53],"but":[54],"only":[56],"rarely":[57],"implemented.":[59],"Subsequent":[60],"sections":[61],"describe":[62],"basic":[63],"Carlo,":[67],"based":[68],"on":[69,88],"Hastings":[71],"algorithm":[72],"including":[74,95],"Metropolis":[77],"method":[78],"Gibbs":[81],"sampler":[82],"special":[84],"cases,":[85],"go":[87],"discuss":[90],"some":[91],"more":[92],"specialized":[93],"developments,":[94],"adaptive":[96],"slice":[97],"sampling,":[98],"exact":[99],"goodness\u2013of\u2013fit":[100],"tests,":[101],"maximum":[102],"likelihood":[103],"estimation,":[104],"Langevin\u2013Hastings":[106],"algorithm,":[107],"auxiliary":[108],"variables":[109],"techniques,":[110],"perfect":[111],"sampling":[112],"via":[113],"coupling":[114],"from":[115],"past,":[117],"reversible":[118],"jumps":[119],"for":[121],"target":[122],"spaces":[123],"of":[124],"varying":[125],"dimensions,":[126],"simulated":[128],"annealing.":[129],"Specimen":[130],"are":[132],"described":[133],"throughout":[134],"notes.":[136]},"counts_by_year":[],"updated_date":"2026-10-12T09:40:04.860316","created_date":"2025-10-27T00:00:00"}