Rao-Blackwellized particle Gibbs kernels for smoothing in jump Markov nonlinear models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F18%3APU128431" target="_blank" >RIV/00216305:26620/18:PU128431 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.23919/ECC.2018.8550408" target="_blank" >http://dx.doi.org/10.23919/ECC.2018.8550408</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/ECC.2018.8550408" target="_blank" >10.23919/ECC.2018.8550408</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Rao-Blackwellized particle Gibbs kernels for smoothing in jump Markov nonlinear models

Popis výsledku v původním jazyce

Jump Markov nonlinear models (JMNMs) characterize a dynamical system by a finite number of presumably nonlinear and possibly non-Gaussian state-space configurations that switch according to a discrete-valued hidden Markov process. In this context, the smoothing problem - the task of estimating fixed points or sequences of hidden variables given all available data -is of key relevance to many objectives of statistical inference, including the estimation of static parameters. The present paper proposes a particle Gibbs with ancestor sampling (PGAS)-based smoother for JMNMs. The design methodology relies on integrating out the discrete process in order to increase the efficiency through Rao-Blackwellization. The experimental evaluation illustrates that the proposed method achieves higher estimation accuracy in less computational time compared to the original PGAS procedure.

Název v anglickém jazyce

Rao-Blackwellized particle Gibbs kernels for smoothing in jump Markov nonlinear models

Popis výsledku anglicky

Jump Markov nonlinear models (JMNMs) characterize a dynamical system by a finite number of presumably nonlinear and possibly non-Gaussian state-space configurations that switch according to a discrete-valued hidden Markov process. In this context, the smoothing problem - the task of estimating fixed points or sequences of hidden variables given all available data -is of key relevance to many objectives of statistical inference, including the estimation of static parameters. The present paper proposes a particle Gibbs with ancestor sampling (PGAS)-based smoother for JMNMs. The design methodology relies on integrating out the discrete process in order to increase the efficiency through Rao-Blackwellization. The experimental evaluation illustrates that the proposed method achieves higher estimation accuracy in less computational time compared to the original PGAS procedure.

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)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 16th European Control Conference, ECC 2018

ISBN

9783952426999

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

2466-2471

Název nakladatele

Institute of Electrical and Electronics Engineers (IEEE)

Místo vydání

neuveden

Místo konání akce

Limassol

Datum konání akce

12. 6. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000467725302082