Original title: Recursive mixture estimation with univariate multimodal Poisson variable
Authors: Uglickich, Evženie ; Nagy, Ivan
Document type: Research reports
Year: 2022
Language: eng
Series: Research Report, volume: 2394
Abstract: 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.
Keywords: clustering and classification; mixture of Poisson distributions; recursive mixture estimation
Project no.: 8A19009
Funding provider: GA MŠk

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2022/ZS/uglickich-0557467.pdf
Original record: http://hdl.handle.net/11104/0331506

Permalink: http://www.nusl.cz/ntk/nusl-508593


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