Recursive mixture estimation with univariate multimodal Poisson variable

0557467 - ÚTIA 2023 RIV CZ eng V - Výzkumná zpráva
Uglickich, Evženie - Nagy, Ivan
Recursive mixture estimation with univariate multimodal Poisson variable.
Prague: UTIA AV ČR, v. v. i.,, 2022. 14 s. Research Report, 2394.
Grant CEP: GA MŠMT(CZ) 8A19009
Klíčová slova: recursive mixture estimation * mixture of Poisson distributions * clustering and classification
Obor OECD: Statistics and probability
http://library.utia.cas.cz/separaty/2022/ZS/uglickich-0557467.pdf

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.
Trvalý link: http://hdl.handle.net/11104/0331506