{"id":"https://openalex.org/W2396726046","doi":null,"title":"A Bayesian nonparametric model for spectral estimation of metastable systems","display_name":"A Bayesian nonparametric model for spectral estimation of metastable systems","publication_year":2014,"publication_date":"2014-07-23","ids":{"openalex":"https://openalex.org/W2396726046","mag":"2396726046"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.484.5022","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.484.5022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://auai.org/uai2014/proceedings/individuals/338.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5060090190","display_name":"Hao Wu","orcid":"https://orcid.org/0000-0002-2170-0618","observed_orcids":["https://orcid.org/0000-0002-2170-0618"]},"institutions":[{"id":"https://openalex.org/I75951250","display_name":"Freie Universit\u00e4t Berlin","ror":"https://ror.org/046ak2485","country_code":"DE","type":"education","lineage":["https://openalex.org/I75951250"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Hao Wu","raw_affiliation_strings":["Department of Mathematics and Computer Science Free University of Berlin Berlin Germany"],"raw_orcid":"https://orcid.org/0000-0002-2170-0618","affiliations":[{"raw_affiliation_string":"Department of Mathematics and Computer Science Free University of Berlin Berlin Germany","institution_ids":["https://openalex.org/I75951250"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5060090190"],"corresponding_institution_ids":["https://openalex.org/I75951250"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.25848276,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"878","last_page":"887"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9688000082969666,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9688000082969666,"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/T12056","display_name":"Markov Chains and Monte Carlo Methods","score":0.018699999898672104,"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/T11236","display_name":"Control Systems and Identification","score":0.003000000026077032,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/metastable-systems","display_name":"metastable systems","score":0.8600000143051147},{"id":"https://openalex.org/keywords/spectral-estimation","display_name":"spectral estimation","score":0.9940000176429749},{"id":"https://openalex.org/keywords/hidden-markov-models","display_name":"hidden Markov models","score":0.7940000295639038},{"id":"https://openalex.org/keywords/infinite-hidden-markov-model","display_name":"infinite hidden Markov model","score":0.8539999723434448},{"id":"https://openalex.org/keywords/stick-breaking-process","display_name":"stick-breaking process","score":0.9010000228881836},{"id":"https://openalex.org/keywords/eigenvalue-estimation","display_name":"eigenvalue estimation","score":0.6159999966621399},{"id":"https://openalex.org/keywords/sparse-priors","display_name":"sparse priors","score":0.6000000238418579},{"id":"https://openalex.org/keywords/nonparametric-bayesian-inference","display_name":"Bayesian nonparametrics","score":0.7350000143051147}],"concepts":[{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6922231912612915},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6171483397483826},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5522598028182983},{"id":"https://openalex.org/C128803854","wikidata":"https://www.wikidata.org/wiki/Q1307821","display_name":"Eigenfunction","level":3,"score":0.5519447326660156},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.5511889457702637},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5458666086196899},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5162334442138672},{"id":"https://openalex.org/C89464430","wikidata":"https://www.wikidata.org/wiki/Q849516","display_name":"Metastability","level":2,"score":0.4826091527938843},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.449382483959198},{"id":"https://openalex.org/C30049272","wikidata":"https://www.wikidata.org/wiki/Q6555326","display_name":"Spectral density estimation","level":3,"score":0.44442018866539},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.423717200756073},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4218302071094513},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41238802671432495},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3835533857345581},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3619195818901062},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3555174469947815},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.2143734097480774},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12415251135826111},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.10267886519432068},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0860125720500946},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.484.5022","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.484.5022","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://auai.org/uai2014/proceedings/individuals/338.pdf","raw_type":"text"},{"id":"mag:2396726046","doi":null,"is_oa":false,"landing_page_url":"http://auai.org/uai2014/proceedings/individuals/338.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306421103","display_name":"Uncertainty in Artificial Intelligence","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Uncertainty in Artificial Intelligence","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W166614460","https://openalex.org/W1698248478","https://openalex.org/W1830165584","https://openalex.org/W1969903391","https://openalex.org/W1971725855","https://openalex.org/W1988220126","https://openalex.org/W1989544083","https://openalex.org/W1992924464","https://openalex.org/W1995748690","https://openalex.org/W2011760672","https://openalex.org/W2012029966","https://openalex.org/W2040188179","https://openalex.org/W2045656233","https://openalex.org/W2069429561","https://openalex.org/W2072169887","https://openalex.org/W2077760098","https://openalex.org/W2085213650","https://openalex.org/W2088941628","https://openalex.org/W2089842282","https://openalex.org/W2107195525","https://openalex.org/W2119945372","https://openalex.org/W2135537007","https://openalex.org/W2151457493","https://openalex.org/W2158266063","https://openalex.org/W3100810942","https://openalex.org/W3104741175"],"related_works":["https://openalex.org/W7095660482","https://openalex.org/W7135855744","https://openalex.org/W2521512188","https://openalex.org/W23740867","https://openalex.org/W2396974021","https://openalex.org/W7135839240","https://openalex.org/W2784495288","https://openalex.org/W3043146404","https://openalex.org/W1696888686","https://openalex.org/W2276336071"],"abstract_inverted_index":{"The":[0,140],"identification":[1],"of":[2,19,31,34,48,84,94,119,146,155,158,171],"eigenvalues":[3,147],"and":[4,14,67,97,125,148],"eigenfunc-tions":[5],"from":[6],"simulation":[7],"or":[8],"experimental":[9],"data":[10],"is":[11,87],"a":[12,29,49,58,89,127,143,162],"fundamental":[13],"important":[15],"problem":[16],"for":[17,122],"anal-ysis":[18],"metastable":[20,36,51,159],"systems,":[21],"because":[22],"the":[23,35,46,68,78,92,98,117,169],"domi-nant":[24],"spectral":[25,69,79,120,175],"components":[26],"usually":[27],"contain":[28],"lot":[30],"essential":[32],"information":[33],"dy-namics":[37],"on":[38,103],"slow":[39],"timescales.":[40],"It":[41],"has":[42],"been":[43],"shown":[44],"that":[45],"dynamics":[47],"strongly":[50],"sys-tem":[52],"can":[53,71,149],"be":[54,72,150],"equivalently":[55],"described":[56],"as":[57],"hidden":[59],"Markov":[60],"model":[61,130,134],"(HMM)":[62],"under":[63],"some":[64],"technical":[65],"as-sumptions":[66],"estimation":[70,80,121],"performed":[73],"through":[74],"HMM":[75],"learning.":[76],"However,":[77],"with":[81],"unknown":[82],"number":[83],"dominant":[85,156],"spectra":[86],"still":[88],"challenge":[90],"in":[91,161],"framework":[93],"traditional":[95],"HMMs,":[96,124],"infi-nite":[99],"HMMs":[100],"developed":[101],"based":[102],"stick-breaking":[104,132],"processes":[105],"cannot":[106],"satisfactorily":[107],"solved":[108],"this":[109,113,138],"prob-lem":[110],"either.":[111],"In":[112],"paper,":[114],"we":[115],"analyze":[116],"diffi-culties":[118],"infinite":[123],"present":[126],"new":[128],"nonparametric":[129,163],"called":[131],"half-weighted":[133],"(SB-HWM)":[135],"to":[136,152,174],"address":[137],"problem.":[139],"SB-HWM":[141,173],"defines":[142],"sparse":[144],"prior":[145],"applied":[151],"Bayesian":[153],"inference":[154],"eigenpairs":[157],"systems":[160],"manner.":[164],"We":[165],"demonstrate":[166],"by":[167],"simulations":[168],"advantages":[170],"applying":[172],"estimation.":[176],"1":[177]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}