Bayesian Mixture Estimation without Tears

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F21%3A00544577" target="_blank" >RIV/67985556:_____/21:00544577 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21260/21:00350675

Výsledek na webu

<a href="http://dx.doi.org/10.5220/0010508706410648" target="_blank" >http://dx.doi.org/10.5220/0010508706410648</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5220/0010508706410648" target="_blank" >10.5220/0010508706410648</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian Mixture Estimation without Tears

Popis výsledku v původním jazyce

This paper aims at presenting the on-line non-iterative form of Bayesian mixture estimation. The model used is composed of a set of sub-models (components) and an estimated pointer variable that currently indicates the active component. The estimation is built on an approximated Bayes rule using weighted measured data. The weights are derived from the so called proximity of measured data entries to individual components. The basis for the generation of the weights are integrated likelihood functions with the inserted point estimates of the component parameters. One of the main advantages of the presented data analysis method is a possibility of a simple incorporation of the available prior knowledge. Simple examples with a programming code as well as results of experiments with real data are demonstrated. The main goal of this paper is to provide clear description of the Bayesian estimation method based on the approximated likelihood functions, called proximities.

Název v anglickém jazyce

Bayesian Mixture Estimation without Tears

Popis výsledku anglicky

This paper aims at presenting the on-line non-iterative form of Bayesian mixture estimation. The model used is composed of a set of sub-models (components) and an estimated pointer variable that currently indicates the active component. The estimation is built on an approximated Bayes rule using weighted measured data. The weights are derived from the so called proximity of measured data entries to individual components. The basis for the generation of the weights are integrated likelihood functions with the inserted point estimates of the component parameters. One of the main advantages of the presented data analysis method is a possibility of a simple incorporation of the available prior knowledge. Simple examples with a programming code as well as results of experiments with real data are demonstrated. The main goal of this paper is to provide clear description of the Bayesian estimation method based on the approximated likelihood functions, called proximities.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/8A19009" target="_blank" >8A19009: Arrowhead Tools for Engineering of Digitalisation Solutions</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics

ISBN

978-989-758-522-7

ISSN

2184-2809

e-ISSN

—

Počet stran výsledku

8

Strana od-do

641-648

Název nakladatele

Scitepress

Místo vydání

Setúbal

Místo konání akce

Setúbal (online)

Datum konání akce

6. 7. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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