Youngmin Ha (2019). Fast multi-output relevance vector regression (https://www.mathworks.com/matlabcentral/fileexchange/49131-fast-multi-output-relevance-vector-regression), MATLAB Central File Exchange. Retrieved .
Yes, Monte Carlo simulation can be parallelized by using parfor.
Instead of the while loop, can the code be modified to run parallel using the Parallel Toolbox?
the paper link is available, but the download does not work,pls renew a link,thx
You may use "Gauss" instead of "+Gauss" and delete "assert(size(Phi,2) == N + 1" if all regression outputs do not have any constant (i.e. bias). You should use "+" if you do not have any ideas about regression outputs.
I have been enjoying your code, I have 2 questions
1. Did you intend for a user to be able to unbiased the Kernel though specifying 'Gauss' instead of '+Gauss'? Removing the '+' sign causes an error in fmrvr on the line
assert(size(Phi,2) == N + 1, 'unexpected matrix size of Phi')
2. In fmrvr there is referenced your paper: "Fast multivariate relevance vector regression," to Annals of Mathematics and Artificial Intelligence (2015). Can you send me a link to this paper as I cannot locate it via searching online.
URL of corresponding paper has been added.
Title has changed as "multi-output" is more proper than "multivariate".
Thumbnail image is changed.
Old license is deleted.