{"id":"https://openalex.org/W3176417267","doi":"https://doi.org/10.1109/tsp.2021.3093792","title":"Policy Gradient Importance Sampling for Bayesian Inference","display_name":"Policy Gradient Importance Sampling for Bayesian Inference","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3176417267","doi":"https://doi.org/10.1109/tsp.2021.3093792","mag":"3176417267"},"language":"en","primary_location":{"id":"doi:10.1109/tsp.2021.3093792","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2021.3093792","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","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/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":false,"raw_author_name":"Yousef El-Laham","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-0728-737X","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA","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, USA"],"raw_orcid":"https://orcid.org/0000-0003-2963-1474","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USA","institution_ids":["https://openalex.org/I59553526"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":0.6824,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.75037914,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"69","issue":null,"first_page":"4245","last_page":"4256"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9994999766349792,"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"}},{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9990000128746033,"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"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9987000226974487,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.7001495361328125},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6069847345352173},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.567520260810852},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.5549295544624329},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5393725633621216},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.5326656103134155},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.5268211364746094},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.5092083215713501},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5013000965118408},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.49525728821754456},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.467795193195343},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3848426342010498},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3449110984802246},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3301244080066681},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12733280658721924},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.10813090205192566}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.7001495361328125},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6069847345352173},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.567520260810852},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.5549295544624329},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5393725633621216},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.5326656103134155},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.5268211364746094},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5092083215713501},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5013000965118408},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.49525728821754456},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.467795193195343},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3848426342010498},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3449110984802246},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3301244080066681},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12733280658721924},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.10813090205192566},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tsp.2021.3093792","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tsp.2021.3093792","pdf_url":null,"source":{"id":"https://openalex.org/S168680287","display_name":"IEEE Transactions on Signal Processing","issn_l":"1053-587X","issn":["1053-587X","1941-0476"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Signal Processing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2893072315","display_name":null,"funder_award_id":"CCF-1617986","funder_id":"https://openalex.org/F4320335353","funder_display_name":"National Science Foundation of Sri Lanka"}],"funders":[{"id":"https://openalex.org/F4320335353","display_name":"National Science Foundation of Sri Lanka","ror":"https://ror.org/010xaa060"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":68,"referenced_works":["https://openalex.org/W187762159","https://openalex.org/W280128266","https://openalex.org/W621546036","https://openalex.org/W788570312","https://openalex.org/W1539591727","https://openalex.org/W1542912205","https://openalex.org/W1594563152","https://openalex.org/W1603746717","https://openalex.org/W1665662210","https://openalex.org/W1979969656","https://openalex.org/W1994616650","https://openalex.org/W1996962424","https://openalex.org/W2006722592","https://openalex.org/W2009797711","https://openalex.org/W2013164703","https://openalex.org/W2022637125","https://openalex.org/W2023678279","https://openalex.org/W2026653933","https://openalex.org/W2056712138","https://openalex.org/W2082399300","https://openalex.org/W2109690032","https://openalex.org/W2111787305","https://openalex.org/W2121863487","https://openalex.org/W2127107099","https://openalex.org/W2137943964","https://openalex.org/W2155027007","https://openalex.org/W2156718681","https://openalex.org/W2156840067","https://openalex.org/W2225156818","https://openalex.org/W2272328839","https://openalex.org/W2279857902","https://openalex.org/W2523246573","https://openalex.org/W2753526058","https://openalex.org/W2788904251","https://openalex.org/W2889345494","https://openalex.org/W2894771547","https://openalex.org/W2936638486","https://openalex.org/W2937032503","https://openalex.org/W2949211412","https://openalex.org/W2963027910","https://openalex.org/W2964250748","https://openalex.org/W2989666437","https://openalex.org/W2990572911","https://openalex.org/W2997177880","https://openalex.org/W3012944593","https://openalex.org/W3101380508","https://openalex.org/W3103126047","https://openalex.org/W3103862105","https://openalex.org/W3106038217","https://openalex.org/W3124691892","https://openalex.org/W3149189374","https://openalex.org/W4206008854","https://openalex.org/W4214717370","https://openalex.org/W4236214058","https://openalex.org/W4238682518","https://openalex.org/W4285719527","https://openalex.org/W4293874240","https://openalex.org/W4299094297","https://openalex.org/W6635824087","https://openalex.org/W6676780147","https://openalex.org/W6682888085","https://openalex.org/W6683204974","https://openalex.org/W6694973836","https://openalex.org/W6727249380","https://openalex.org/W6748600884","https://openalex.org/W6765877897","https://openalex.org/W6775323287","https://openalex.org/W6803432515"],"related_works":["https://openalex.org/W2051058708","https://openalex.org/W154868527","https://openalex.org/W1494268238","https://openalex.org/W1983207144","https://openalex.org/W2490706771","https://openalex.org/W2480116122","https://openalex.org/W1976468483","https://openalex.org/W1516574938","https://openalex.org/W2563912921","https://openalex.org/W4382752342"],"abstract_inverted_index":{"In":[0],"this":[1,32],"paper,":[2],"we":[3],"propose":[4],"a":[5,18,27,50,62,92],"novel":[6],"adaptive":[7],"importance":[8],"sampling":[9],"(AIS)":[10],"algorithm":[11],"for":[12,116],"probabilistic":[13],"inference.":[14],"The":[15],"sampler":[16,39],"learns":[17],"proposal":[19,35],"distribution":[20,36],"adaptation":[21],"strategy":[22],"by":[23],"framing":[24],"AIS":[25,75],"as":[26,42],"reinforcement":[28],"learning":[29],"problem.":[30],"Under":[31],"structure,":[33],"the":[34,38,57,59,71,74,78,82,98,102,117,121],"of":[37,56,73,77,81,101,104,120],"is":[40,47,65],"treated":[41],"an":[43],"agent":[44,60],"whose":[45],"state":[46],"controlled":[48],"using":[49],"parameterized":[51],"policy.":[52],"At":[53],"each":[54],"iteration":[55],"algorithm,":[58],"earns":[61],"reward":[63],"that":[64,96],"related":[66],"to":[67,70,90,124],"its":[68],"contribution":[69],"variance":[72],"estimator":[76],"normalization":[79],"constant":[80],"target":[83],"distribution.":[84],"Policy":[85],"gradient":[86],"methods":[87],"are":[88],"employed":[89],"learn":[91],"locally":[93],"optimal":[94],"policy":[95],"maximizes":[97],"expected":[99],"value":[100],"sum":[103],"all":[105],"rewards.":[106],"Numerical":[107],"simulations":[108],"on":[109],"two":[110],"different":[111],"examples":[112],"demonstrate":[113],"promising":[114],"results":[115],"future":[118],"application":[119],"proposed":[122],"method":[123],"complex":[125],"Bayesian":[126],"models.":[127]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
