Adaptive importance sampling for Bayesian inference in Gaussian process models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F14%3A43922643" target="_blank" >RIV/49777513:23220/14:43922643 - isvavai.cz</a>

Výsledek na webu

<a href="http://10.3182/20140824-6-ZA-1003.02352" target="_blank" >http://10.3182/20140824-6-ZA-1003.02352</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3182/20140824-6-ZA-1003.02352" target="_blank" >10.3182/20140824-6-ZA-1003.02352</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive importance sampling for Bayesian inference in Gaussian process models

Popis výsledku v původním jazyce

Gaussian process (GP) models are nowadays considered among the standard tools in modern control system engineering. They are routinely used for model-based control, time- series prediction, modelling and estimation in engineering applications. While theunderlying theory is completely in line with the principles of Bayesian inference, in practice this property is lost due to approximation steps in the GP inference. In this paper we propose a novel inference algorithm for GP models, which relies on adaptive importance sampling strategy to numerically evaluate the intractable marginalization over the hyperparameters. This is required in the case of broad-peaked or multi-modal posterior distribution of the hyperparameters where the point approximations turn out to be insufficient. The benefits of the algorithm are that is retains the Bayesian nature of the inference, has sufficient convergence properties, relatively low computational load and does not require heavy prior knowledge due to

Název v anglickém jazyce

Adaptive importance sampling for Bayesian inference in Gaussian process models

Popis výsledku anglicky

Gaussian process (GP) models are nowadays considered among the standard tools in modern control system engineering. They are routinely used for model-based control, time- series prediction, modelling and estimation in engineering applications. While theunderlying theory is completely in line with the principles of Bayesian inference, in practice this property is lost due to approximation steps in the GP inference. In this paper we propose a novel inference algorithm for GP models, which relies on adaptive importance sampling strategy to numerically evaluate the intractable marginalization over the hyperparameters. This is required in the case of broad-peaked or multi-modal posterior distribution of the hyperparameters where the point approximations turn out to be insufficient. The benefits of the algorithm are that is retains the Bayesian nature of the inference, has sufficient convergence properties, relatively low computational load and does not require heavy prior knowledge due to

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/ED2.1.00%2F03.0094" target="_blank" >ED2.1.00/03.0094: Regionální inovační centrum elektrotechniky (RICE)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 19th IFAC World Congress, 2014

ISBN

978-3-902823-62-5

ISSN

1474-6670

e-ISSN

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Počet stran výsledku

6

Strana od-do

5011-5016

Název nakladatele

Elsevier

Místo vydání

Amsterdam

Místo konání akce

Cape Town

Datum konání akce

24. 8. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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