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Recursive mixture estimation with univariate multimodal Poisson variable

SYSNO ASEP	0557467
Document Type	V - Research Report
R&D Document Type	O - Ostatní
Title	Recursive mixture estimation with univariate multimodal Poisson variable
Author(s)	Uglickich, Evženie (UTIA-B)_ORCID Nagy, Ivan (UTIA-B)_{RID, ORCID}
Number of authors	2
Issue data	Prague: UTIA AV ČR, v. v. i.,, 2022
Series	Research Report
Series number	2394
Number of pages	14 s.
Publication form	Print - P
Language	eng - English
Country	CZ - Czech Republic
Keywords	recursive mixture estimation ; mixture of Poisson distributions ; clustering and classification
Subject RIV	BB - Applied Statistics, Operational Research
OECD category	Statistics and probability
R&D Projects	8A19009 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
Annotation	Analysis of count variables described by the Poisson distribution is required in many application fields. Examples of the count variables observed per a time unit can be, e.g., number of customers, passengers, road accidents, Internet traffic packet arrivals, bankruptcies, virus attacks, etc. If the behavior of such a variable exhibits a multimodal character, the problem of clustering and classification of incoming count data arises. This issue can touch, for instance, detecting clusters of the different behavior of drivers in traffic flow analysis as well as cyclists or pedestrians. This work focuses on the model-based clustering of Poisson-distributed count data with the help of the recursive Bayesian estimation of the mixture of Poisson components. The aim of the work is to explain the methodology in details with an illustrative simple example, so that the work is limited to the univariate case and static pointer.
Workplace	Institute of Information Theory and Automation
Contact	Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
Year of Publishing	2023

Number of the records: 1