Sequential Monte Carlo estimation of transition probabilities in mixture filtering problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU119992" target="_blank" >RIV/00216305:26220/16:PU119992 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7528003&isnumber=7527857" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7528003&isnumber=7527857</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Sequential Monte Carlo estimation of transition probabilities in mixture filtering problems

Popis výsledku v původním jazyce

Physical systems switching between various working regimes are often encountered in practical applications. However, transition probabilities, according to which a system switches from the current regime to another one, are commonly designed as a priori known parameters, and their misspecification can degrade the performance of the algorithms filtering (or estimating) latent variables of the system. To overcome the misspecification, the present paper proposes a novel Sequential Monte Carlo procedure for estimating the transition probabilities. More specifically, it extends the concept of Rao-Blackwellization to the Dirichlet distribution, which represents the model of these probabilities. The experiments show that the proposed technique outperforms some of the classical methods in terms of the estimation precision and also the precision stability.

Název v anglickém jazyce

Sequential Monte Carlo estimation of transition probabilities in mixture filtering problems

Popis výsledku anglicky

Physical systems switching between various working regimes are often encountered in practical applications. However, transition probabilities, according to which a system switches from the current regime to another one, are commonly designed as a priori known parameters, and their misspecification can degrade the performance of the algorithms filtering (or estimating) latent variables of the system. To overcome the misspecification, the present paper proposes a novel Sequential Monte Carlo procedure for estimating the transition probabilities. More specifically, it extends the concept of Rao-Blackwellization to the Dirichlet distribution, which represents the model of these probabilities. The experiments show that the proposed technique outperforms some of the classical methods in terms of the estimation precision and also the precision stability.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 19th International Conference on Information Fusion, FUSION 2016

ISBN

978-1-5090-2012-6

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1063-1070

Název nakladatele

International Society of Information Fusion

Místo vydání

Heidelberg

Místo konání akce

Heidelberg

Datum konání akce

5. 7. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000391273400142