{"id":"https://openalex.org/W2974220163","doi":"https://doi.org/10.1109/twc.2020.3012910","title":"The Max-Product Algorithm Viewed as Linear Data-Fusion: A Distributed Detection Scenario","display_name":"The Max-Product Algorithm Viewed as Linear Data-Fusion: A Distributed Detection Scenario","publication_year":2020,"publication_date":"2020-08-05","ids":{"openalex":"https://openalex.org/W2974220163","doi":"https://doi.org/10.1109/twc.2020.3012910","mag":"2974220163"},"language":"en","primary_location":{"id":"doi:10.1109/twc.2020.3012910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/twc.2020.3012910","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"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 Wireless Communications","raw_type":"journal-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1909.09402","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5083019624","display_name":"Younes Abdi","orcid":"https://orcid.org/0000-0001-8171-3886"},"institutions":[{"id":"https://openalex.org/I94722563","display_name":"University of Jyv\u00e4skyl\u00e4","ror":"https://ror.org/05n3dz165","country_code":"FI","type":"education","lineage":["https://openalex.org/I94722563"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Younes Abdi","raw_affiliation_strings":["Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, Jyv\u00e4skyl\u00e4, Finland","University of Jyv\u00e4skyl\u00e4"],"raw_orcid":"https://orcid.org/0000-0001-8171-3886","affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, Jyv\u00e4skyl\u00e4, Finland","institution_ids":["https://openalex.org/I94722563"]},{"raw_affiliation_string":"University of Jyv\u00e4skyl\u00e4","institution_ids":["https://openalex.org/I94722563"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5086891852","display_name":"Tapani Ristaniemi","orcid":"https://orcid.org/0000-0002-8461-3968"},"institutions":[{"id":"https://openalex.org/I94722563","display_name":"University of Jyv\u00e4skyl\u00e4","ror":"https://ror.org/05n3dz165","country_code":"FI","type":"education","lineage":["https://openalex.org/I94722563"]}],"countries":["FI"],"is_corresponding":false,"raw_author_name":"Tapani Ristaniemi","raw_affiliation_strings":["Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, Jyv\u00e4skyl\u00e4, Finland","University of Jyv\u00e4skyl\u00e4"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Information Technology, University of Jyv\u00e4skyl\u00e4, Jyv\u00e4skyl\u00e4, Finland","institution_ids":["https://openalex.org/I94722563"]},{"raw_affiliation_string":"University of Jyv\u00e4skyl\u00e4","institution_ids":["https://openalex.org/I94722563"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5083019624"],"corresponding_institution_ids":["https://openalex.org/I94722563"],"apc_list":null,"apc_paid":null,"fwci":0.4856,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66211511,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"19","issue":"11","first_page":"7585","last_page":"7597"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.9975000023841858,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9836000204086304,"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/pairwise-comparison","display_name":"Pairwise comparison","score":0.6463843584060669},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.6366558074951172},{"id":"https://openalex.org/keywords/product","display_name":"Product (mathematics)","score":0.6159657835960388},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.5020108222961426},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.49021273851394653},{"id":"https://openalex.org/keywords/node","display_name":"Node (physics)","score":0.47110989689826965},{"id":"https://openalex.org/keywords/linear-programming","display_name":"Linear programming","score":0.4484095275402069},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.44672122597694397},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3963261842727661},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.35752350091934204},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.20739337801933289},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1654185950756073},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.12556636333465576},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09892964363098145}],"concepts":[{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.6463843584060669},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.6366558074951172},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.6159657835960388},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.5020108222961426},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49021273851394653},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.47110989689826965},{"id":"https://openalex.org/C41045048","wikidata":"https://www.wikidata.org/wiki/Q202843","display_name":"Linear programming","level":2,"score":0.4484095275402069},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.44672122597694397},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3963261842727661},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35752350091934204},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.20739337801933289},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1654185950756073},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.12556636333465576},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09892964363098145},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.1109/twc.2020.3012910","is_oa":false,"landing_page_url":"https://doi.org/10.1109/twc.2020.3012910","pdf_url":null,"source":{"id":"https://openalex.org/S63459445","display_name":"IEEE Transactions on Wireless Communications","issn_l":"1536-1276","issn":["1536-1276","1558-2248"],"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 Wireless Communications","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:1909.09402","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.09402","pdf_url":"https://arxiv.org/pdf/1909.09402","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:jyx.jyu.fi:123456789/71401","is_oa":true,"landing_page_url":"http://urn.fi/URN:NBN:fi:jyu-202008175537","pdf_url":"https://jyx.jyu.fi/bitstreams/83a1b0db-aa7e-4984-96f5-f3635a554d7b/download","source":{"id":"https://openalex.org/S4306400563","display_name":"Jyv\u00e4skyl\u00e4 University Digital Archive (University of Jyv\u00e4skyl\u00e4)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I94722563","host_organization_name":"University of Jyv\u00e4skyl\u00e4","host_organization_lineage":["https://openalex.org/I94722563"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"A1"},{"id":"mag:2974220163","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1909.09402","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1909.09402","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1909.09402","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1909.09402","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1909.09402","pdf_url":"https://arxiv.org/pdf/1909.09402","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1525535255","https://openalex.org/W1971538083","https://openalex.org/W1983289383","https://openalex.org/W2006509007","https://openalex.org/W2016044775","https://openalex.org/W2016101562","https://openalex.org/W2017747313","https://openalex.org/W2030500923","https://openalex.org/W2076839709","https://openalex.org/W2082029531","https://openalex.org/W2098387242","https://openalex.org/W2103227205","https://openalex.org/W2107639356","https://openalex.org/W2108619558","https://openalex.org/W2110567841","https://openalex.org/W2118388220","https://openalex.org/W2120340025","https://openalex.org/W2125826911","https://openalex.org/W2127421917","https://openalex.org/W2134396534","https://openalex.org/W2144424895","https://openalex.org/W2150397642","https://openalex.org/W2150859678","https://openalex.org/W2165202277","https://openalex.org/W2166670884","https://openalex.org/W2167149929","https://openalex.org/W2560932009","https://openalex.org/W2610971674","https://openalex.org/W2642385489","https://openalex.org/W2898398693","https://openalex.org/W2909316670","https://openalex.org/W2951090971","https://openalex.org/W2962945558","https://openalex.org/W2963206527","https://openalex.org/W2973536543","https://openalex.org/W2979987893","https://openalex.org/W2991457285","https://openalex.org/W3098848552","https://openalex.org/W4293052541","https://openalex.org/W6640992714","https://openalex.org/W6684101231"],"related_works":["https://openalex.org/W3047729131","https://openalex.org/W2368538030","https://openalex.org/W1503345968","https://openalex.org/W2641152181","https://openalex.org/W3049632073","https://openalex.org/W2770734487","https://openalex.org/W2125200135","https://openalex.org/W2753622836","https://openalex.org/W2982547441","https://openalex.org/W2124279213","https://openalex.org/W2231243630","https://openalex.org/W2791764391","https://openalex.org/W1921024081","https://openalex.org/W2963462051","https://openalex.org/W2120947722","https://openalex.org/W3093022283","https://openalex.org/W2952697322","https://openalex.org/W3038128227","https://openalex.org/W3194382524","https://openalex.org/W2761142108"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,28,67,127,163],"disclose":[4],"the":[5,9,50,58,64,74,80,90,94,98,110,113,120,123,130,134,145],"statistical":[6],"behavior":[7,111,121],"of":[8,24,57,79,97,112,122,133,144],"max-product":[10,38,99,114,135],"algorithm":[11,100,115],"configured":[12],"to":[13,72,119],"solve":[14],"a":[15,17,22,31,37,41,141],"maximum":[16],"posteriori":[18],"estimation":[19],"problem":[20],"in":[21,63,77,89],"network":[23],"distributed":[25,32],"agents.":[26],"Specifically,":[27],"first":[29],"build":[30],"hypothesis":[33,91],"test":[34,92],"conducted":[35],"by":[36,103],"iteration":[39,136],"over":[40],"binary-valued":[42],"pairwise":[43],"Markov":[44],"random":[45],"field":[46],"and":[47,82,109],"show":[48],"that":[49,129],"decision":[51],"variables":[52],"obtained":[53,102],"are":[54],"linear":[55,70,106,142],"combinations":[56,71],"local":[59],"log-likelihood":[60],"ratios":[61],"observed":[62],"network.":[65],"Then,":[66],"use":[68],"these":[69],"formulate":[73],"system":[75],"performance":[76,96,132],"terms":[78],"false-alarm":[81],"detection":[83],"probabilities.":[84],"Our":[85],"findings":[86],"indicate":[87],"that,":[88],"concerned,":[93],"optimal":[95,105,131],"is":[101,116,137,149],"an":[104],"data-fusion":[107],"scheme":[108],"very":[117],"similar":[118],"sum-product":[124,146],"algorithm.":[125],"Consequently,":[126],"demonstrate":[128],"closely":[138],"achieved":[139],"via":[140,167],"version":[143],"algorithm,":[147],"which":[148],"optimized":[150],"based":[151],"on":[152],"statistics":[153],"received":[154],"at":[155],"each":[156],"node":[157],"from":[158],"its":[159],"one-hop":[160],"neighbors.":[161],"Finally,":[162],"verify":[164],"our":[165],"observations":[166],"computer":[168],"simulations.":[169]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}