Inference of Aggregate Hidden Markov Models With Continuous Observations

We consider a class of inference problems for large populations where each individual is modeled by the same hidden Markov model (HMM). We focus on aggregate inference problems in HMMs with discrete state space and continuous observation space. The continuous observations are aggregated in a way suc...

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Veröffentlicht in:	IEEE control systems letters 2022, Vol.6, p.2377-2382
Hauptverfasser:	Zhang, Qinsheng, Singh, Rahul, Chen, Yongxin
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Sprache:	eng
Schlagworte:	Aggregates filtering Hidden Markov models Inference algorithms Markov process Markov processes Noise measurement Sociology Statistics stochastic systems
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creator	Zhang, Qinsheng Singh, Rahul Chen, Yongxin
description	We consider a class of inference problems for large populations where each individual is modeled by the same hidden Markov model (HMM). We focus on aggregate inference problems in HMMs with discrete state space and continuous observation space. The continuous observations are aggregated in a way such that the individuals are indistinguishable from measurements. We propose an aggregate inference algorithm called continuous observation collective forward-backward algorithm. It extends the recently proposed collective forward-backward algorithm for aggregate inference in HMMs with discrete observations to the case of continuous observations.
doi_str_mv	10.1109/LCSYS.2022.3158197
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It extends the recently proposed collective forward-backward algorithm for aggregate inference in HMMs with discrete observations to the case of continuous observations.</description><subject>Aggregates</subject><subject>filtering</subject><subject>Hidden Markov models</subject><subject>Inference algorithms</subject><subject>Markov process</subject><subject>Markov processes</subject><subject>Noise measurement</subject><subject>Sociology</subject><subject>Statistics</subject><subject>stochastic systems</subject><issn>2475-1456</issn><issn>2475-1456</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM1Kw0AUhQdRsNS-gG7mBVLnzmSSzLIENYWWLqqIqzA_NzFaMzKTFnz7traIq3Pg8J3FR8gtsCkAU_eLcv22nnLG-VSALEDlF2TE01wmkMrs8l-_JpMYPxhjUPCccTUi1bxvMGBvkfqGzto2YKsHpFXnHPZ0qcOn39Gld7iJ9LUb3mnp-6Hrt34b6cpEDDs9dL6PN-Sq0ZuIk3OOycvjw3NZJYvV07ycLRIrAIbEZMwUOmUuN6JQQgmUTQ6FVYZpm2rBnba5sYipSgWIgkFmpHWOq6xRWioxJvz0a4OPMWBTf4fuS4efGlh91FH_6qiPOuqzjgN0d4I6RPwDDgNIYGIPRQBcOw</recordid><startdate>2022</startdate><enddate>2022</enddate><creator>Zhang, Qinsheng</creator><creator>Singh, Rahul</creator><creator>Chen, Yongxin</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><orcidid>https://orcid.org/0000-0002-1459-6365</orcidid></search><sort><creationdate>2022</creationdate><title>Inference of Aggregate Hidden Markov Models With Continuous Observations</title><author>Zhang, Qinsheng ; Singh, Rahul ; Chen, Yongxin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c311t-b60b8a40d7b389393e5f718c9b0ac4a32dac7bcee4943138016b5cdd296f9a593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aggregates</topic><topic>filtering</topic><topic>Hidden Markov models</topic><topic>Inference algorithms</topic><topic>Markov process</topic><topic>Markov processes</topic><topic>Noise measurement</topic><topic>Sociology</topic><topic>Statistics</topic><topic>stochastic systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Qinsheng</creatorcontrib><creatorcontrib>Singh, Rahul</creatorcontrib><creatorcontrib>Chen, Yongxin</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><jtitle>IEEE control systems letters</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zhang, Qinsheng</au><au>Singh, Rahul</au><au>Chen, Yongxin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Inference of Aggregate Hidden Markov Models With Continuous Observations</atitle><jtitle>IEEE control systems letters</jtitle><stitle>LCSYS</stitle><date>2022</date><risdate>2022</risdate><volume>6</volume><spage>2377</spage><epage>2382</epage><pages>2377-2382</pages><issn>2475-1456</issn><eissn>2475-1456</eissn><coden>ICSLBO</coden><abstract>We consider a class of inference problems for large populations where each individual is modeled by the same hidden Markov model (HMM). 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subjects	Aggregates filtering Hidden Markov models Inference algorithms Markov process Markov processes Noise measurement Sociology Statistics stochastic systems
title	Inference of Aggregate Hidden Markov Models With Continuous Observations
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