{"id":"https://openalex.org/W87158075","doi":null,"title":"Bayesian posterior comprehension via Message from Monte Carlo","display_name":"Bayesian posterior comprehension via Message from Monte Carlo","publication_year":2003,"publication_date":"2003-01-01","ids":{"openalex":"https://openalex.org/W87158075","mag":"87158075"},"language":"es","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.13.2557","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.2557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.csse.monash.edu.au/~leighf/papers/Fitzgibbon03b.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/A5084717336","display_name":"Leigh J. Fitzgibbon","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Leigh J. Fitzgibbon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038085743","display_name":"David L. Dowe","orcid":"https://orcid.org/0000-0002-0583-5918","observed_orcids":["https://orcid.org/0000-0002-0583-5918"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David L. Dowe","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-0583-5918","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5065385022","display_name":"Lloyd Allison","orcid":"https://orcid.org/0000-0002-9020-3164","observed_orcids":["https://orcid.org/0000-0002-9020-3164"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lloyd Allison","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-9020-3164","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"21"},"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":0.8794000148773193,"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":0.8794000148773193,"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":0.06599999964237213,"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":0.020800000056624413,"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/posterior-summaries","display_name":"posterior summaries","score":0.6209999918937683},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.7250000238418579},{"id":"https://openalex.org/keywords/posterior-approximation","display_name":"posterior approximation","score":0.45100000500679016},{"id":"https://openalex.org/keywords/posterior-expectations","display_name":"posterior expectations","score":0.5879999995231628},{"id":"https://openalex.org/keywords/point-estimation","display_name":"point estimation","score":0.7829999923706055},{"id":"https://openalex.org/keywords/change-point-problem","display_name":"change-point problem","score":0.5770000219345093},{"id":"https://openalex.org/keywords/binary-regression","display_name":"binary regression","score":0.9210000038146973},{"id":"https://openalex.org/keywords/generalized-linear-models","display_name":"generalized linear models","score":0.8059999942779541},{"id":"https://openalex.org/keywords/codebook-construction","display_name":"codebook construction","score":0.6679999828338623}],"concepts":[{"id":"https://openalex.org/C2775858994","wikidata":"https://www.wikidata.org/wiki/Q5383732","display_name":"Epitome","level":2,"score":0.9047946929931641},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.752868115901947},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6289277076721191},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6116024255752563},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5229806900024414},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.5062317252159119},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42353346943855286},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39937007427215576},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33513450622558594},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24664229154586792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.20859584212303162}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.13.2557","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.2557","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.csse.monash.edu.au/~leighf/papers/Fitzgibbon03b.pdf","raw_type":"text"},{"id":"mag:87158075","doi":null,"is_oa":false,"landing_page_url":"https://www.csse.monash.edu.au/~lloyd/tildeMML/2003HICS-l/2003HICS-l1.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W156498718","https://openalex.org/W1484280155","https://openalex.org/W1494622843","https://openalex.org/W1498065827","https://openalex.org/W1800261609","https://openalex.org/W1983114617","https://openalex.org/W1990495631","https://openalex.org/W1997404989","https://openalex.org/W2105518356","https://openalex.org/W2106706098","https://openalex.org/W2110381504","https://openalex.org/W2111051773","https://openalex.org/W2128860595","https://openalex.org/W2130416410","https://openalex.org/W2158940042","https://openalex.org/W2163284277"],"related_works":["https://openalex.org/W7099356121","https://openalex.org/W7096013320","https://openalex.org/W7097760184","https://openalex.org/W2301201700","https://openalex.org/W6908619073","https://openalex.org/W1908974388","https://openalex.org/W7097795475","https://openalex.org/W36562234","https://openalex.org/W7099131411","https://openalex.org/W7095435707"],"abstract_inverted_index":{"We":[0,70,95,125],"discuss":[1],"the":[2,25,32,43,63,98,107],"problem":[3],"of":[4,11,65,74,84,92,100],"producing":[5],"an":[6,38,78,116],"epitome,":[7],"or":[8],"brief":[9],"summary,":[10],"a":[12,20,72,135],"Bayesian":[13,101],"posterior":[14,93],"distribution":[15],"-":[16,82],"and":[17,89,104,128,166],"then":[18,126],"investigate":[19],"general":[21,49],"solution":[22],"based":[23],"on":[24],"Minimum":[26,108,140],"Message":[27,109,130,141,153],"Length":[28,110,142],"(MML)":[29],"principle.":[30],"Clearly,":[31],"optimal":[33],"criterion":[34,67,118],"for":[35,137,160],"choosing":[36],"such":[37],"epitome":[39,66,79,117],"is":[40,51,55,158],"determined":[41],"by":[42],"epitome's":[44],"intended":[45],"use.":[46],"The":[47,152],"interesting":[48],"case":[50],"where":[52],"this":[53],"use":[54],"unknown":[56],"since,":[57],"in":[58],"order":[59],"to":[60],"be":[61,113],"practical,":[62],"choice":[64],"becomes":[68],"subjective.":[69],"identify":[71],"number":[73],"desirable":[75],"properties":[76,99],"that":[77,106,119],"could":[80],"have":[81],"facilitation":[83],"point":[85],"estimation,":[86],"human":[87],"comprehension,":[88],"fast":[90],"approximation":[91],"expectations.":[94],"call":[96],"these":[97,123],"Posterior":[102],"Comprehension":[103],"show":[105],"principle":[111],"can":[112],"viewed":[114],"as":[115,134],"produces":[120],"epitomes":[121],"having":[122],"properties.":[124],"present":[127],"extend":[129],"from":[131,154],"Monte":[132,149,155],"Carlo":[133,150,156],"means":[136],"constructing":[138],"instantaneous":[139],"codebooks":[143],"(and":[144],"epitomes)":[145],"using":[146],"Markov":[147],"Chain":[148],"methods.":[151],"methodology":[157],"illustrated":[159],"binary":[161],"regression,":[162],"generalised":[163],"linear":[164],"model,":[165],"multiple":[167],"change-point":[168],"problems.":[169]},"counts_by_year":[{"year":2013,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
