https://github.com/agarbuno/paims_codes
Parallel asymptotically independent Markov sampling for Gaussian process hyper-parameters
Science Score: 23.0%
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Low similarity (10.9%) to scientific vocabulary
Repository
Parallel asymptotically independent Markov sampling for Gaussian process hyper-parameters
Basic Info
- Host: GitHub
- Owner: agarbuno
- Language: Matlab
- Default Branch: master
- Size: 28.3 KB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
paims_codes
Overview
This repository contains an extension to the Asymptotically Independent Markov Sampling (AIMS) algorithm for stochastic optimisation purposes and has been currently developed for the estimation of the hyper-parameters of a Gaussian process emulator. This work is part of my PhD research project at the University of Liverpool. Currently the only version available is on Matlab but it is expected to be written in R and Python in the near future.
Contents
- A startup file to load all directories needed for the sampler.
- The
matlab-filesdirectory contains all files needed for the examples to run. Being
Matlab [ ... ] = parallel_aims_opt( ... )the main code for the sampler. - The
examplesdirectory contains several examples used in the paper submitted with the results obtained from such extension.
Citing
We would be grateful if any results based on this parallel adaptive extension are acknowledged by citing our paper. It is currently available here
TeX
@Article{Garbuno2016a,
author = {A. Garbuno-Inigo and F.A. DiazDelaO and K.M. Zuev},
title = {Gaussian process hyper-parameter estimation using Parallel Asymptotically Independent Markov Sampling },
journal = {Computational Statistics \& Data Analysis },
year = {2016},
volume = {103},
pages = {367 - 383},
doi = {http://dx.doi.org/10.1016/j.csda.2016.05.019},
issn = {0167-9473},
keywords = {Gaussian process},
url = {http://www.sciencedirect.com/science/article/pii/S0167947316301311}
}
Owner
- Name: Alfredo Garbuno Iñigo
- Login: agarbuno
- Kind: user
- Location: Mexico City
- Company: ITAM
- Website: agarbuno.github.io
- Twitter: AlfredoGarbuno
- Repositories: 71
- Profile: https://github.com/agarbuno
Bayesian inference, non-parametric Bayesian models, MCMC algorithms, Kernel Methods, Data assimilation, Langevin dynamics