SPGP_dist.zip

 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