{"id":"https://openalex.org/W2914564660","doi":"https://doi.org/10.1109/cdc.2018.8619316","title":"Likelihood Rate Based Estimation of Nonstationary Markov Models","display_name":"Likelihood Rate Based Estimation of Nonstationary Markov Models","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2914564660","doi":"https://doi.org/10.1109/cdc.2018.8619316","mag":"2914564660"},"language":"en","primary_location":{"id":"doi:10.1109/cdc.2018.8619316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2018.8619316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Conference on Decision and Control (CDC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5071263618","display_name":"Harshal Maske","orcid":null},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Harshal Maske","raw_affiliation_strings":["Distributed Autonomous Systems (DAS) laboratory, University of Illinois, Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Distributed Autonomous Systems (DAS) laboratory, University of Illinois, Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073739058","display_name":"Girish Chowdhary","orcid":"https://orcid.org/0000-0002-4657-307X"},"institutions":[{"id":"https://openalex.org/I157725225","display_name":"University of Illinois Urbana-Champaign","ror":"https://ror.org/047426m28","country_code":"US","type":"education","lineage":["https://openalex.org/I157725225"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Girish Chowdhary","raw_affiliation_strings":["Distributed Autonomous Systems (DAS) laboratory, University of Illinois, Urbana-Champaign"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Distributed Autonomous Systems (DAS) laboratory, University of Illinois, Urbana-Champaign","institution_ids":["https://openalex.org/I157725225"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157725225"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"77","issue":null,"first_page":"4759","last_page":"4766"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9959999918937683,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9840999841690063,"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"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9797000288963318,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical 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/markov-chain","display_name":"Markov chain","score":0.7053471803665161},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6249181032180786},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5674675703048706},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.5575342774391174},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5381719470024109},{"id":"https://openalex.org/keywords/variable-order-markov-model","display_name":"Variable-order Markov model","score":0.5098906755447388},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5052836537361145},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4620714783668518},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.46199631690979004},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4411686360836029},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.4291546046733856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3913707435131073},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3776330351829529},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.34540820121765137},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2834787368774414},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1642138957977295}],"concepts":[{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.7053471803665161},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6249181032180786},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5674675703048706},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.5575342774391174},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5381719470024109},{"id":"https://openalex.org/C54907487","wikidata":"https://www.wikidata.org/wiki/Q7915688","display_name":"Variable-order Markov model","level":4,"score":0.5098906755447388},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5052836537361145},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4620714783668518},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.46199631690979004},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4411686360836029},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.4291546046733856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3913707435131073},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3776330351829529},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.34540820121765137},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2834787368774414},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1642138957977295}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cdc.2018.8619316","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2018.8619316","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Conference on Decision and Control (CDC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W135124929","https://openalex.org/W605692607","https://openalex.org/W1503398984","https://openalex.org/W1526144783","https://openalex.org/W1577863597","https://openalex.org/W1759730364","https://openalex.org/W1967042859","https://openalex.org/W2045893916","https://openalex.org/W2125838338","https://openalex.org/W2132036885","https://openalex.org/W2132957691","https://openalex.org/W2134069175","https://openalex.org/W2139688922","https://openalex.org/W2142561902","https://openalex.org/W2149670996","https://openalex.org/W2154099718","https://openalex.org/W2158190429","https://openalex.org/W2158712098","https://openalex.org/W2171166366","https://openalex.org/W2891763203","https://openalex.org/W2972891548","https://openalex.org/W3104501812","https://openalex.org/W6631529720","https://openalex.org/W6637559989","https://openalex.org/W6680805354","https://openalex.org/W6683269077"],"related_works":["https://openalex.org/W2082284720","https://openalex.org/W2116722627","https://openalex.org/W2537260108","https://openalex.org/W2158700654","https://openalex.org/W2379938888","https://openalex.org/W1510894296","https://openalex.org/W2134386692","https://openalex.org/W2194396582","https://openalex.org/W2799426416","https://openalex.org/W2566202039"],"abstract_inverted_index":{"Although":[0],"Markov":[1,37,68,83,147],"models":[2],"are":[3],"widely":[4],"used":[5],"and":[6,58,61,97,112],"researched,":[7],"improving":[8],"their":[9],"capability":[10],"to":[11,33,53,59,70],"guarantee":[12],"optimal":[13],"performance":[14],"in":[15,36,80],"real":[16],"world":[17],"processes":[18],"relies":[19],"on":[20,110],"perfect":[21],"state":[22],"inference":[23,120],"amidst":[24],"non-stationarity.":[25],"This":[26],"paper":[27],"develops":[28],"a":[29,88,102,141],"novel":[30,89],"estimation":[31,91],"technique":[32],"capture":[34],"non-stationarity":[35,57],"sequences":[38],"induced":[39],"by":[40],"switching":[41],"transition":[42,76],"probability":[43,77],"matrices":[44,78],"(TPMs).":[45],"We":[46,65,86],"introduce":[47],"the":[48,74,119,129],"concept":[49],"of":[50,56,101],"likelihood":[51,106],"rate":[52],"establish":[54],"existence":[55],"detect":[60],"estimate":[62],"multiple":[63,95],"TPMs.":[64],"layer":[66],"another":[67],"chain":[69],"model":[71,144],"switches":[72],"between":[73],"estimated":[75],"resulting":[79],"Layered":[81],"Non-stationary":[82],"Models":[84],"(LNMM).":[85],"present":[87],"non-parametric":[90],"process":[92],"that":[93,118],"evaluates":[94],"priors":[96],"performs":[98],"Bayesian":[99],"update":[100],"prior":[103],"with":[104],"highest":[105],"rate.":[107],"Our":[108],"experiments":[109],"synthetic":[111],"honey":[113],"bee":[114],"dance":[115],"dataset":[116],"shows":[117],"using":[121],"LNMM":[122],"is":[123],"two":[124],"times":[125],"more":[126],"accurate":[127],"than":[128],"existing":[130],"unsupervised":[131],"learning":[132],"methods":[133],"while":[134],"being":[135],"computationally":[136],"efficient,":[137],"validating":[138],"it":[139],"as":[140],"highly":[142],"expressive":[143],"for":[145],"non-stationary":[146],"sequences.":[148]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
