I’m trying to build a latent GP that I’m going to use to model temporal variation in a larger model. So, rather than using GPRegression, I’m taking the kernel and using it to generate covariance matrices for a MultivariateNormal, as follows:
# Population trend in metric ls = pyro.sample('ls', Gamma(torch.FloatTensor([4.]), torch.FloatTensor([1.]))) amp = pyro.sample('amp', Exponential(torch.FloatTensor([1.]))) K = gp.kernels.RBF(input_dim=1, variance=amp, lengthscale=ls) beta = pyro.sample('beta', MultivariateNormal(torch.zeros(W), covariance_matrix=K(torch.FloatTensor(weeks))))
however, when I do this, I almost always get a singular value problem with the Cholesky:
cholesky_cpu: U(5,5) is zero, singular U.
What’s the best way to stabilize this? Should I be adding a tiny diagonal value to the generated matrix, or should I be adding a WhiteNoise kernel to the RBF?