Variační Bayesovská Filtrace

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F08%3A00313233" target="_blank" >RIV/67985556:_____/08:00313233 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Variational Bayesian Filtering

Popis výsledku v původním jazyce

The use of the Variational Bayes (VB) approximation in Bayesian filtering is studied, both as a means to accelerate marginalized particle filtering, and as a deterministic local (one-step) approximation. The VB method of approximation and its variants are reviewed. These variants provide a range of algorithms that can be used in a principled trade-off between quality of approximation and computational cost. In combination with marginalized particle filtering, they generalize previously published work onvariational filtering, and they extend currently available methods for speeding up stochastic approximations in Bayesian filtering. In particular, the free-form nature of the VB approximation allows optimal selection of moments which summarize the particles. The performance of the various VB filtering schemes is illustrated in the context of a Gaussian model with a nonlinear sub-state, and a hidden Markov model.

Název v anglickém jazyce

Variational Bayesian Filtering

Popis výsledku anglicky

The use of the Variational Bayes (VB) approximation in Bayesian filtering is studied, both as a means to accelerate marginalized particle filtering, and as a deterministic local (one-step) approximation. The VB method of approximation and its variants are reviewed. These variants provide a range of algorithms that can be used in a principled trade-off between quality of approximation and computational cost. In combination with marginalized particle filtering, they generalize previously published work onvariational filtering, and they extend currently available methods for speeding up stochastic approximations in Bayesian filtering. In particular, the free-form nature of the VB approximation allows optimal selection of moments which summarize the particles. The performance of the various VB filtering schemes is illustrated in the context of a Gaussian model with a nonlinear sub-state, and a hidden Markov model.

Druh

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

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

—

Projekt

<a href="/cs/project/1M0572" target="_blank" >1M0572: Data, algoritmy, rozhodování</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Signal Processing

ISSN

1053-587X

e-ISSN

—

Svazek periodika

56

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

—

Kód UT WoS článku

000259407400003

EID výsledku v databázi Scopus

—