Gaussian Process Binary Classification Pseudo Code

I am working with various inference methods for classification. Currently I am trying to combine MCMC for producing samples from a posterior distribution with with a Gaussian Process binary classifier. However, I am struggling at how a pseudo code may look. I have successfully implemented the MCMC algorithm and have generated the relevant posterior samples. Now I don't know how to implement the GP classifier once I have these samples. The co-variance function is the squared error function and the mean function is zero.

Any help/tips would be greatly appreciated

Answer 1

[Harsh]

Hello,

The following resources maybe useful to you:

https://www.mathworks.com/help/stats/classification.html

https://www.mathworks.com/help/stats/gaussian-process-regression-models.html

http://www.gaussianprocess.org/gpml/code/matlab/doc/

https://github.com/SheffieldML/GPmat