Metadata Updated: February 28, 2019

The code in the stableGP package implements Gaussian process calculations using efficient and numerically stable algorithms. Description of the algorithms is in the paper "Stable and Efficient Gaussian Process Calculations" by L. Foster, A. Waagen, N. Aijaz, M. Hurley, A. Luis, J. Rinsky, C. Satyavolu, M. Way, P. Gazis, and A. Srivastava accepted in the Journal of Machine Learning Research, February, 2009.

The easiest way to get started using the code is to download and unzip the zip file, start Matlab (7.0 or higher), move to the appropriate folder and type either "demo_bootstrap" or "demo_history" to run one of the demonstration files.

Code in the zip file is based on the code used in the text Gaussian Processes or Machine Learning by Rasmussen and Williams ( So that the demonstrations are self contained and do not require that the user download additional code the zip file contains a few functions that are copied directly from Rasmussen and Williams' code.

Access & Use Information

Public: This dataset is intended for public access and use. License: U.S. Government Work

Downloads & Resources


Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date August 1, 2018
Metadata Updated Date February 28, 2019
Publisher Dashlink
Unique Identifier DASHLINK_123
Ashok Srivastava
Maintainer Email
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context
Metadata Catalog ID
Schema Version
Catalog Describedby
Harvest Object Id 0c5cd209-5d87-4997-b902-32ec4c6a2d15
Harvest Source Id 39e4ad2a-47ca-4507-8258-852babd0fd99
Harvest Source Title NASA Data.json
Data First Published 2010-09-10
Homepage URL
Data Last Modified 2018-07-19
Program Code 026:029
Source Datajson Identifier True
Source Hash 19c1e4cae96d8dedf41ed871bd064b05a0297e80
Source Schema Version 1.1

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