资 源 简 介
这是一个关于稀疏高斯过程的matlab源码，可以用于计算测试输入的高斯预测值
"spgp_pred" computes the SPGP predictive distribution for a set of
test inputs. You need to supply a set of "pseudo-inputs" or "basis
vectors" for the approximation, and suitable hyperparameters for the
covariance. You can use any method you like for finding the
"pseudo-inputs", with the simplest obviously being a random subset of
the data. It is coded for Gaussian covariance function, but you could
very easily alter this. It is also fine to use for high dimensional
data sets.
"spgp_lik" is the SPGP (negative) marginal likelihood and gradients
with respect to pseudo-inputs and hyperparameters. So you can use this
if you wish to try to optimize the positioning of pseudo-inputs and
find good hyperparameters, before using "spgp_pred". I would recommend
initializing the pseudo-inputs on a random subset of the data, and
initializing
