Marginalized approximate filtering of state-space models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F18%3A00478074" target="_blank" >RIV/67985556:_____/18:00478074 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1002/acs.2821" target="_blank" >http://dx.doi.org/10.1002/acs.2821</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/acs.2821" target="_blank" >10.1002/acs.2821</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Marginalized approximate filtering of state-space models

Popis výsledku v původním jazyce

The marginalized particle filtering (MPF) is a powerful technique reducing the number of particles necessary to effectively estimate hidden states of state-space models. This paper alleviates the assumption of a fully known and computationally tractable observation model. Exploiting the recent developments in the theory of approximate Bayesian computation (ABC) filtration, an ABC counterpart of MPF is proposed, applicable when the observation model is too complex to be evaluated analytically or even numerically, but it is still possible to sample from it by plugging in the state. The novelty is 2-fold. First, ABC methods have not been used in marginalized filtering yet. Second, a new multivariate robust method for evaluation of particle weights is proposed. The goal of this paper is to demonstrate the idea on the background of the MPF with a particular accent on exposition.

Název v anglickém jazyce

Marginalized approximate filtering of state-space models

Popis výsledku anglicky

The marginalized particle filtering (MPF) is a powerful technique reducing the number of particles necessary to effectively estimate hidden states of state-space models. This paper alleviates the assumption of a fully known and computationally tractable observation model. Exploiting the recent developments in the theory of approximate Bayesian computation (ABC) filtration, an ABC counterpart of MPF is proposed, applicable when the observation model is too complex to be evaluated analytically or even numerically, but it is still possible to sample from it by plugging in the state. The novelty is 2-fold. First, ABC methods have not been used in marginalized filtering yet. Second, a new multivariate robust method for evaluation of particle weights is proposed. The goal of this paper is to demonstrate the idea on the background of the MPF with a particular accent on exposition.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA16-09848S" target="_blank" >GA16-09848S: Racionalita a uvažování</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 periodika

International Journal of Adaptive Control and Signal Processing

ISSN

0890-6327

e-ISSN

—

Svazek periodika

32

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

13

Strana od-do

1-12

Kód UT WoS článku

000419919900001

EID výsledku v databázi Scopus

2-s2.0-85030092933