On the Use of Particle Markov Chain Monte Carlo in Parameter Estimation of Space-Time Interacting Discs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10283166" target="_blank" >RIV/00216208:11320/14:10283166 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/14:00222638 RIV/60461373:22340/14:43897646

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11009-013-9367-2" target="_blank" >http://dx.doi.org/10.1007/s11009-013-9367-2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11009-013-9367-2" target="_blank" >10.1007/s11009-013-9367-2</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On the Use of Particle Markov Chain Monte Carlo in Parameter Estimation of Space-Time Interacting Discs

Popis výsledku v původním jazyce

A space-time random set is defined and methods of its parameters estimation are investigated. The evolution in discrete time is described by a state-space model. The observed output is a planar union of interacting discs given by a probability density with respect to a reference Poisson process of discs. The state vector is to be estimated together with auxiliary parameters of transitions caused by a random walk. Three methods of parameters estimation are involved, first of which is the maximum likelihood estimation (MLE) for individual outputs at fixed times. In the space-time model the state vector can be estimated by the particle filter (PF), where MLE serves to the estimation of auxiliary parameters. In the present paper the aim is to compare MLE and PF with particle Markov chain Monte Carlo (PMCMC). From the group of PMCMC methods we use specially the particle marginal Metropolis-Hastings (PMMH) algorithm which updates simultaneously the state vector and the auxiliary parameters.

Název v anglickém jazyce

On the Use of Particle Markov Chain Monte Carlo in Parameter Estimation of Space-Time Interacting Discs

Popis výsledku anglicky

A space-time random set is defined and methods of its parameters estimation are investigated. The evolution in discrete time is described by a state-space model. The observed output is a planar union of interacting discs given by a probability density with respect to a reference Poisson process of discs. The state vector is to be estimated together with auxiliary parameters of transitions caused by a random walk. Three methods of parameters estimation are involved, first of which is the maximum likelihood estimation (MLE) for individual outputs at fixed times. In the space-time model the state vector can be estimated by the particle filter (PF), where MLE serves to the estimation of auxiliary parameters. In the present paper the aim is to compare MLE and PF with particle Markov chain Monte Carlo (PMCMC). From the group of PMCMC methods we use specially the particle marginal Metropolis-Hastings (PMMH) algorithm which updates simultaneously the state vector and the auxiliary parameters.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP201%2F10%2F0472" target="_blank" >GAP201/10/0472: Stochastická geometrie - nehomogenita, kótování, dynamika a stereologie</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název periodika

Methodology and Computing in Applied Probability

ISSN

1387-5841

e-ISSN

—

Svazek periodika

16

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

451-463

Kód UT WoS článku

000335508800013

EID výsledku v databázi Scopus

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