Adaptive multiple importance sampling for Gaussian processes
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Adaptive multiple importance sampling for Gaussian processes
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10103 - Statistics and probability
Result continuities
Project
<a href="/en/project/7F14287" target="_blank" >7F14287: Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI)</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Statistical Computation and Simulation
ISSN
0094-9655
e-ISSN
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Volume of the periodical
87
Issue of the periodical within the volume
8
Country of publishing house
GB - UNITED KINGDOM
Number of pages
22
Pages from-to
1644-1665
UT code for WoS article
000399503500009
EID of the result in the Scopus database
2-s2.0-85010689209