Estimation of discrete data using binomial mixture

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21260%2F20%3A00341899" target="_blank" >RIV/68407700:21260/20:00341899 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/SCSP49987.2020.9133938" target="_blank" >https://doi.org/10.1109/SCSP49987.2020.9133938</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SCSP49987.2020.9133938" target="_blank" >10.1109/SCSP49987.2020.9133938</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Estimation of discrete data using binomial mixture

Popis výsledku v původním jazyce

Data analysis is an important method for obtaining information which is useful in many projects such as e.g. Smart Cities. Common data sources are questionnaires, their output mainly purveys discrete data. The most common description of discrete data is through categorical models. These models have several advantages such as flexibility but there are also disadvantages such as a huge dimension of the table expressing this distribution for more variables and values and their overparametrization. The aim of this paper is to replace the categorical distribution by another discrete distribution with a lower number of parameters while maintaining the model quality. Binomial distribution was chosen a suitable one because it is determined only by one parameter and this parameter allows to shape the probability function of binomial distribution well. The output of the paper is the presented model of mixture with binomial components. The suggested estimation algorithms are tested on real traffic data.

Název v anglickém jazyce

Estimation of discrete data using binomial mixture

Popis výsledku anglicky

Data analysis is an important method for obtaining information which is useful in many projects such as e.g. Smart Cities. Common data sources are questionnaires, their output mainly purveys discrete data. The most common description of discrete data is through categorical models. These models have several advantages such as flexibility but there are also disadvantages such as a huge dimension of the table expressing this distribution for more variables and values and their overparametrization. The aim of this paper is to replace the categorical distribution by another discrete distribution with a lower number of parameters while maintaining the model quality. Binomial distribution was chosen a suitable one because it is determined only by one parameter and this parameter allows to shape the probability function of binomial distribution well. The output of the paper is the presented model of mixture with binomial components. The suggested estimation algorithms are tested on real traffic data.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2020 Smart City Symposium Prague

ISBN

978-1-7281-6821-0

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

—

Název nakladatele

IEEE Press

Místo vydání

New York

Místo konání akce

Prague

Datum konání akce

25. 6. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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