| bibtype |
J -
Journal Article
|
| ARLID |
0469804 |
| utime |
20240103213444.8 |
| mtime |
20170125235959.9 |
| SCOPUS |
85010689209 |
| WOS |
000399503500009 |
| DOI |
10.1080/00949655.2017.1280037 |
| title
(primary) (eng) |
Adaptive multiple importance sampling for Gaussian processes |
| specification |
| page_count |
22 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0254060 |
| ISSN |
0094-9655 |
| title
|
Journal of Statistical Computation and Simulation |
| volume_id |
87 |
| volume |
8 (2017) |
| page_num |
1644-1665 |
| publisher |
|
|
| keyword |
Gaussian Process |
| keyword |
Bayesian estimation |
| keyword |
Adaptive importance sampling |
| author
(primary) |
| ARLID |
cav_un_auth*0345417 |
| name1 |
Xiong |
| name2 |
X. |
| country |
GB |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| institution |
UTIA-B |
| full_dept |
Department of Adaptive Systems |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0345418 |
| name1 |
Filippone |
| name2 |
M. |
| country |
FR |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0318110 |
| project_id |
7F14287 |
| agency |
GA MŠk |
| country |
CZ |
|
| abstract
(eng) |
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. |
| RIV |
BB |
| FORD0 |
10000 |
| FORD1 |
10100 |
| FORD2 |
10103 |
| reportyear |
2018 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0270731 |
| confidential |
S |
| mrcbC86 |
3+4 Article Computer Science Interdisciplinary Applications|Statistics Probability |
| mrcbC86 |
3+4 Article Computer Science Interdisciplinary Applications|Statistics Probability |
| mrcbC86 |
3+4 Article Computer Science Interdisciplinary Applications|Statistics Probability |
| mrcbT16-e |
COMPUTERSCIENCEINTERDISCIPLINARYAPPLICATIONS|STATISTICSPROBABILITY |
| mrcbT16-j |
0.414 |
| mrcbT16-s |
0.704 |
| mrcbT16-B |
24.502 |
| mrcbT16-D |
Q4 |
| mrcbT16-E |
Q2 |
| arlyear |
2017 |
| mrcbU14 |
85010689209 SCOPUS |
| mrcbU24 |
PUBMED |
| mrcbU34 |
000399503500009 WOS |
| mrcbU63 |
cav_un_epca*0254060 Journal of Statistical Computation and Simulation 0094-9655 1563-5163 Roč. 87 č. 8 2017 1644 1665 Taylor & Francis |
|