{"id":"https://openalex.org/W1494195487","doi":"https://doi.org/10.1007/978-3-642-02490-0_85","title":"Design of Exchange Monte Carlo Method for Bayesian Learning in Normal Mixture Models","display_name":"Design of Exchange Monte Carlo Method for Bayesian Learning in Normal Mixture Models","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W1494195487","doi":"https://doi.org/10.1007/978-3-642-02490-0_85","mag":"1494195487"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-642-02490-0_85","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-642-02490-0_85","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","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/A5050911583","display_name":"Kenji Nagata","orcid":"https://orcid.org/0000-0001-9894-4461"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kenji Nagata","raw_affiliation_strings":["P. & I Lab., Tokyo Institute of Technology, MailBox: R2-5, 4259, Nagatsuta-chou, Midori-ku, Yokohama, 226-8503, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"P. & I Lab., Tokyo Institute of Technology, MailBox: R2-5, 4259, Nagatsuta-chou, Midori-ku, Yokohama, 226-8503, Japan","institution_ids":["https://openalex.org/I114531698"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089768991","display_name":"Sumio Watanabe","orcid":"https://orcid.org/0000-0001-8341-5639"},"institutions":[{"id":"https://openalex.org/I114531698","display_name":"Tokyo Institute of Technology","ror":"https://ror.org/0112mx960","country_code":"JP","type":"education","lineage":["https://openalex.org/I114531698"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Sumio Watanabe","raw_affiliation_strings":["P. & I Lab., Tokyo Institute of Technology, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"P. & I Lab., Tokyo Institute of Technology, Japan","institution_ids":["https://openalex.org/I114531698"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I114531698"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":null,"fwci":2.818,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.93445378,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"696","last_page":"706"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10591","display_name":"Theoretical and Computational Physics","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T10591","display_name":"Theoretical and Computational Physics","score":0.9987999796867371,"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"}},{"id":"https://openalex.org/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.9728999733924866,"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":0.9635999798774719,"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":0.7849024534225464},{"id":"https://openalex.org/keywords/gibbs-sampling","display_name":"Gibbs sampling","score":0.7599448561668396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7052125930786133},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.685230553150177},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.5975422859191895},{"id":"https://openalex.org/keywords/monte-carlo-molecular-modeling","display_name":"Monte Carlo molecular modeling","score":0.5076183080673218},{"id":"https://openalex.org/keywords/parallel-tempering","display_name":"Parallel tempering","score":0.5008144378662109},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.465553343296051},{"id":"https://openalex.org/keywords/monte-carlo-method-in-statistical-physics","display_name":"Monte Carlo method in statistical physics","score":0.4525163173675537},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4239111840724945},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40611815452575684},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3331357538700104},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1774265170097351},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11776489019393921}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.7849024534225464},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.7599448561668396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7052125930786133},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.685230553150177},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.5975422859191895},{"id":"https://openalex.org/C37669827","wikidata":"https://www.wikidata.org/wiki/Q6904703","display_name":"Monte Carlo molecular modeling","level":4,"score":0.5076183080673218},{"id":"https://openalex.org/C187653413","wikidata":"https://www.wikidata.org/wiki/Q7135015","display_name":"Parallel tempering","level":5,"score":0.5008144378662109},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.465553343296051},{"id":"https://openalex.org/C204493344","wikidata":"https://www.wikidata.org/wiki/Q6904698","display_name":"Monte Carlo method in statistical physics","level":5,"score":0.4525163173675537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4239111840724945},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40611815452575684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3331357538700104},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1774265170097351},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11776489019393921}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-642-02490-0_85","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-642-02490-0_85","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W130710483","https://openalex.org/W1978102570","https://openalex.org/W1985093013","https://openalex.org/W2002664623","https://openalex.org/W2120217353","https://openalex.org/W2132256376","https://openalex.org/W3101006260","https://openalex.org/W4292691288"],"related_works":["https://openalex.org/W1526488073","https://openalex.org/W2078644085","https://openalex.org/W2974901631","https://openalex.org/W2504969732","https://openalex.org/W79101515","https://openalex.org/W1548661257","https://openalex.org/W2001072740","https://openalex.org/W2108651330","https://openalex.org/W1666636243","https://openalex.org/W2080132440"],"abstract_inverted_index":null,"counts_by_year":[{"year":2015,"cited_by_count":1},{"year":2012,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}