{"id":"https://openalex.org/W3015431994","doi":"https://doi.org/10.1109/icassp40776.2020.9053410","title":"Enhanced Mixture Population Monte Carlo Via Stochastic Optimization and Markov Chain Monte Carlo Sampling","display_name":"Enhanced Mixture Population Monte Carlo Via Stochastic Optimization and Markov Chain Monte Carlo Sampling","publication_year":2020,"publication_date":"2020-04-09","ids":{"openalex":"https://openalex.org/W3015431994","doi":"https://doi.org/10.1109/icassp40776.2020.9053410","mag":"3015431994"},"language":"en","primary_location":{"id":"doi:10.1109/icassp40776.2020.9053410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-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/A5069588600","display_name":"Yousef El-Laham","orcid":"https://orcid.org/0000-0002-0728-737X"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yousef El-Laham","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006962534","display_name":"Petar M. Djuri\u0107","orcid":"https://orcid.org/0000-0001-7791-3199"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Petar M. Djuric","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058778434","display_name":"M\u00f3nica F. Bugallo","orcid":"https://orcid.org/0000-0003-2963-1474"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Monica F. Bugallo","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069588600"],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.04625421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"41","issue":null,"first_page":"5475","last_page":"5479"},"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":0.9994000196456909,"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":0.9994000196456909,"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/T10597","display_name":"Nuclear reactor physics and engineering","score":0.9894999861717224,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12404","display_name":"Mathematical Approximation and Integration","score":0.9876999855041504,"subfield":{"id":"https://openalex.org/subfields/2612","display_name":"Numerical Analysis"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"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":0.8344824910163879},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6714495420455933},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5894262790679932},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.570168673992157},{"id":"https://openalex.org/keywords/rejection-sampling","display_name":"Rejection sampling","score":0.5094683766365051},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5033790469169617},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.49001991748809814},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4762149751186371},{"id":"https://openalex.org/keywords/slice-sampling","display_name":"Slice sampling","score":0.4750416576862335},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.4564622938632965},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.4391757845878601},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4319700300693512},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.4208712875843048},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.42079994082450867},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3379390835762024},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3331798315048218},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14075982570648193},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.07482761144638062}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8344824910163879},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6714495420455933},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5894262790679932},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.570168673992157},{"id":"https://openalex.org/C187192777","wikidata":"https://www.wikidata.org/wiki/Q381699","display_name":"Rejection sampling","level":5,"score":0.5094683766365051},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5033790469169617},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.49001991748809814},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4762149751186371},{"id":"https://openalex.org/C170593435","wikidata":"https://www.wikidata.org/wiki/Q4128565","display_name":"Slice sampling","level":4,"score":0.4750416576862335},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.4564622938632965},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.4391757845878601},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4319700300693512},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.4208712875843048},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.42079994082450867},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3379390835762024},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3331798315048218},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14075982570648193},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.07482761144638062},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp40776.2020.9053410","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp40776.2020.9053410","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W187762159","https://openalex.org/W280128266","https://openalex.org/W1539591727","https://openalex.org/W1542912205","https://openalex.org/W1545319692","https://openalex.org/W1552672157","https://openalex.org/W1603746717","https://openalex.org/W1628268552","https://openalex.org/W1665662210","https://openalex.org/W1973594349","https://openalex.org/W1979969656","https://openalex.org/W2056760934","https://openalex.org/W2059448777","https://openalex.org/W2135973421","https://openalex.org/W2138309709","https://openalex.org/W2156840067","https://openalex.org/W2204383650","https://openalex.org/W2212216402","https://openalex.org/W2735102987","https://openalex.org/W2936638486","https://openalex.org/W2962749006","https://openalex.org/W2989666437","https://openalex.org/W3103126047","https://openalex.org/W3106038217","https://openalex.org/W4238682518","https://openalex.org/W4248681815","https://openalex.org/W4293874240","https://openalex.org/W4299094297","https://openalex.org/W6635824087","https://openalex.org/W6687668471","https://openalex.org/W6740256410"],"related_works":["https://openalex.org/W4226314133","https://openalex.org/W3097509027","https://openalex.org/W4295750535","https://openalex.org/W3037866298","https://openalex.org/W2959831473","https://openalex.org/W4288282435","https://openalex.org/W2592308920","https://openalex.org/W1539868720","https://openalex.org/W4385281907","https://openalex.org/W2505405165"],"abstract_inverted_index":{"The":[0,37,64,111],"population":[1,124],"Monte":[2,128],"Carlo":[3,129],"(PMC)":[4],"algorithm":[5,90],"is":[6,147],"a":[7,48,53,71,87,96,123],"popular":[8],"adaptive":[9],"importance":[10],"sampling":[11],"(AIS)":[12],"method":[13],"used":[14],"for":[15,42],"approximate":[16],"computation":[17],"of":[18,34,47,74,95,109,116,125],"intractable":[19],"integrals.":[20],"Over":[21],"the":[22,30,45,61,93,117,133,143],"years,":[23],"many":[24],"advances":[25],"have":[26],"been":[27],"made":[28],"in":[29,52,66],"theory":[31],"and":[32],"implementation":[33],"PMC":[35,39],"schemes.":[36],"mixture":[38,49,97,118],"(M-PMC)":[40],"algorithm,":[41],"instance,":[43],"optimizes":[44,92],"parameters":[46,65,94,119],"proposal":[50,98],"distribution":[51,146],"way":[54],"that":[55,57,91],"minimizes":[56],"Kullback-Leibler":[58],"divergence":[59],"to":[60],"target":[62,145],"distribution.":[63],"M-PMC":[67,89],"are":[68,103,120],"updated":[69],"using":[70,122],"single":[72],"step":[73],"expectation":[75],"maximization":[76],"(EM),":[77],"which":[78],"limits":[79],"its":[80],"accuracy.":[81],"In":[82],"this":[83],"work,":[84],"we":[85],"introduce":[86],"novel":[88],"distribution,":[99],"where":[100,142],"parameter":[101],"updates":[102],"resolved":[104],"via":[105,136],"stochastic":[106,112],"optimization":[107],"instead":[108],"EM.":[110],"gradients":[113],"w.r.t.":[114],"each":[115],"approximated":[121],"Markov":[126],"chain":[127],"samplers.":[130],"We":[131],"validate":[132],"proposed":[134],"scheme":[135],"numerical":[137],"simulations":[138],"on":[139],"an":[140],"example":[141],"considered":[144],"multimodal.":[148]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}