Adaptive multiple importance sampling for Gaussian processes

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00469804" target="_blank" >RIV/67985556:_____/17:00469804 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1080/00949655.2017.1280037" target="_blank" >http://dx.doi.org/10.1080/00949655.2017.1280037</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/00949655.2017.1280037" target="_blank" >10.1080/00949655.2017.1280037</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Adaptive multiple importance sampling for Gaussian processes

Popis výsledku v původním jazyce

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.

Název v anglickém jazyce

Adaptive multiple importance sampling for Gaussian processes

Popis výsledku anglicky

In applications of Gaussian processes (GPs) where quantification of uncertainty is a strict requirement, it is necessary to accurately characterize the posterior distribution over Gaussian process covariance parameters. This is normally done by means of standard Markov chain Monte Carlo (MCMC) algorithms, which require repeated expensive calculations involving the marginal likelihood. Motivated by the desire to avoid the inefficiencies of MCMC algorithms rejecting a considerable amount of expensive proposals, this paper develops an alternative inference framework based on adaptive multiple importance sampling (AMIS). In particular, this paper studies the application of AMIS for GPs in the case of a Gaussian likelihood, and proposes a novel pseudo-marginal-based AMIS algorithm for non-Gaussian likelihoods, where the marginal likelihood is unbiasedly estimated. The results suggest that the proposed framework outperforms MCMC-based inference of covariance parameters in a wide range of scenarios.

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/7F14287" target="_blank" >7F14287: Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI)</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Journal of Statistical Computation and Simulation

ISSN

0094-9655

e-ISSN

—

Svazek periodika

87

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

22

Strana od-do

1644-1665

Kód UT WoS článku

000399503500009

EID výsledku v databázi Scopus

2-s2.0-85010689209