{"id":"https://openalex.org/W4386066216","doi":"https://doi.org/10.1109/isit54713.2023.10206865","title":"Comparing Poisson and Gaussian channels","display_name":"Comparing Poisson and Gaussian channels","publication_year":2023,"publication_date":"2023-06-25","ids":{"openalex":"https://openalex.org/W4386066216","doi":"https://doi.org/10.1109/isit54713.2023.10206865"},"language":"en","primary_location":{"id":"doi:10.1109/isit54713.2023.10206865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit54713.2023.10206865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Information Theory (ISIT)","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/A5112950176","display_name":"Anzo Teh","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110987","display_name":"IIT@MIT","ror":"https://ror.org/01wp8zh54","country_code":"US","type":"facility","lineage":["https://openalex.org/I30771326","https://openalex.org/I4210110987"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Anzo Teh","raw_affiliation_strings":["MIT,EECS,Cambridge,Massachusetts,USA","EECS, MIT, Cambridge, Massachusetts, USA"],"affiliations":[{"raw_affiliation_string":"MIT,EECS,Cambridge,Massachusetts,USA","institution_ids":["https://openalex.org/I4210110987"]},{"raw_affiliation_string":"EECS, MIT, Cambridge, Massachusetts, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031031216","display_name":"Yury Polyanskiy","orcid":null},"institutions":[{"id":"https://openalex.org/I4210110987","display_name":"IIT@MIT","ror":"https://ror.org/01wp8zh54","country_code":"US","type":"facility","lineage":["https://openalex.org/I30771326","https://openalex.org/I4210110987"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yury Polyanskiy","raw_affiliation_strings":["MIT,EECS,Cambridge,Massachusetts,USA","EECS, MIT, Cambridge, Massachusetts, USA"],"affiliations":[{"raw_affiliation_string":"MIT,EECS,Cambridge,Massachusetts,USA","institution_ids":["https://openalex.org/I4210110987"]},{"raw_affiliation_string":"EECS, MIT, Cambridge, Massachusetts, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5112950176"],"corresponding_institution_ids":["https://openalex.org/I4210110987"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0982847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"36","last_page":"41"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.996399998664856,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.996399998664856,"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":0.9843999743461609,"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/T11210","display_name":"Mathematical Analysis and Transform Methods","score":0.9832000136375427,"subfield":{"id":"https://openalex.org/subfields/2604","display_name":"Applied Mathematics"},"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/poisson-distribution","display_name":"Poisson distribution","score":0.5750117897987366},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5563788414001465},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5084656476974487},{"id":"https://openalex.org/keywords/statistical-physics","display_name":"Statistical physics","score":0.3879760801792145},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3112119734287262},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.22408181428909302},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.21516048908233643},{"id":"https://openalex.org/keywords/quantum-mechanics","display_name":"Quantum mechanics","score":0.05696246027946472}],"concepts":[{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.5750117897987366},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5563788414001465},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5084656476974487},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3879760801792145},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3112119734287262},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.22408181428909302},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.21516048908233643},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.05696246027946472}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isit54713.2023.10206865","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isit54713.2023.10206865","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Symposium on Information Theory (ISIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2031680849","https://openalex.org/W2035253879","https://openalex.org/W2065057602","https://openalex.org/W2098346479","https://openalex.org/W3008527562","https://openalex.org/W3118264784","https://openalex.org/W3198285973","https://openalex.org/W4233762729","https://openalex.org/W4245470519","https://openalex.org/W4296142578","https://openalex.org/W4297630743","https://openalex.org/W4300691479","https://openalex.org/W4385248841","https://openalex.org/W6734591652","https://openalex.org/W6778230313","https://openalex.org/W6788252670","https://openalex.org/W6800641761","https://openalex.org/W6843414523"],"related_works":["https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4391375266","https://openalex.org/W1979597421","https://openalex.org/W2007980826","https://openalex.org/W2061531152","https://openalex.org/W3002753104","https://openalex.org/W2077600819","https://openalex.org/W4382765808","https://openalex.org/W82723519"],"abstract_inverted_index":{"Consider":[0],"a":[1,10,34,67,85,96],"pair":[2],"of":[3,89,95],"input":[4],"distributions":[5,46],"which":[6],"after":[7,31],"passing":[8,32],"through":[9,33],"Poisson":[11,97],"channel":[12,36,53],"become":[13],"\u03f5-close":[14,48],"in":[15,99],"total":[16],"variation.":[17],"We":[18],"show":[19,44],"that":[20,45,70],"they":[21],"must":[22,54],"necessarily":[23],"then":[24],"be":[25],"\u03f5":[26,56],"<sup":[27,57,106,111],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[28,58,107,112],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">0.5+o(1)</sup>":[29],"-close":[30,60],"Gaussian":[35,52,76,100],"as":[37],"well.":[38],"In":[39],"the":[40,51,63],"opposite":[41],"direction,":[42],"we":[43,83],"inducing":[47],"outputs":[49,61],"over":[50,62],"induce":[55],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1+o(1)</sup>":[59],"Poisson.":[64],"This":[65],"quantifies":[66],"well-known":[68],"intuition":[69],"\"smoothing\"":[71],"induced":[72],"by":[73],"Poissonization":[74],"and":[75],"convolution":[77],"are":[78],"similar.":[79],"As":[80],"an":[81],"application,":[82],"improve":[84],"recent":[86],"upper":[87],"bound":[88],"Han-Miao-Shen\u20192021":[90],"for":[91],"estimating":[92],"mixing":[93],"distribution":[94],"mixture":[98],"optimal":[101],"transport":[102],"distance":[103],"from":[104],"n":[105,110],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">\u22120.1+o(1)</sup>":[108],"to":[109],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">\u22120.25+o(1)</sup>":[113],".":[114]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}