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Keith Neo Kian Seng

Hamilton Institute
NUI, Maynooth
Co. Kildare
Ireland

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Home Page | Research

Research

A compilation of the Source Codes are available here. I have sub-sectioned them into different toolboxes, ranging from general-purpose Gaussian Process Toolbox, to sophisticated fast algorithms in Gaussian Process Schur Toolbox. They are very useful for learning more about Gaussian process prior models within the Bayesian context, and also provides an insight to Machine Learning.



Source Codes

The sources are available mainly in two formats; that is, M-file (for MATLAB) and MEX-C (C language). The latter is more efficient in large-scale computations. MEX-C (and not C) is used because it allows calling of compiled C binaries from MATLAB environment.

NEW!!! Access the Utility Toolboxes Websites


Gaussian Process Toolbox

Contains basic GP functions.

Gaussian Process Toeplitz Toolbox

Fast algorithms using modified Durbin-Levinson's algorithm. This algorithm was initially developed by Y. Zhang, but has been ported by me into C language for better computational efficiency.

Gaussian Process Schur Toolbox

Another fast algorithm based on the Generalised Schur algorithm. This is algorithm is much more robust than the modified Durbin-Levinson's algorithm as it is capable of handling time-series datasets with missing gaps, allows user-supplied Hessian for optimisation and computation of the standard deviation for the posterior.

Gaussian Process SSTS Toolbox

SSTS stands for State-Space Time-Series, a novel model using compound covariance matrix to incorporate time-series and state-space information of a data, to allow analysis of large-scale state-space datasets.



This page is maintained by Keith Neo.

Last updated: March 15th, 2007