Hi, we have experimental observations where it is physically impossible to have a “lengthscale” below a certain value. However, when I try to add this limit to kernel priors,
kernel.set_prior( "lengthscale", dist.Uniform( torch.tensor(lscale), torch.tensor(lscale) ).to_event() )
, where lscale = [5., 20.] for 1d or lscale = [[5., 5., 5.,], [20., 20., 20.]] for 3d, and then run SparseGPRegression with this kernel, it doesn’t optimize the lengthscale parameter during the SVI steps. It just stays at lscale value (while amplitude and noise get optimized). If I change the lower limit to any value below 1., something like .99, then it starts optimizing the lengthscale parameter as well. I tried it with RBF, Rational Quadratic and Matern kernels and it is always the same. I am wondering how then I can put a lower limit on the lengthscale parameter (which is based on my knowledge about the physical system that I study) ? Thanks!