I’m trying to infer a set of covariance matrixes for latent factors. My model looks something like the following for a single latent factor.
mu ~ Normal sigma ~ Wishart for (n=1...N) x ~ MultivariateNormal(mu, tf.matrix_inverse(sigma)) ...
ed.KLqp and as long as I’m trying to infer
sigma, I observe that inference runs successfully for a some number of iterations, and then I hit:
InvalidArgumentError (see above for traceback): Cholesky decomposition was not successful. The input might not be valid.
I assume this means that the covariance matrix input to the multivariate normal isn’t positive definite (or otherwise acceptable?). I’ve tried both
sigma. I’ve also tried using
tf.cholesky and omitting
tf.matrix_inverse, just for kicks; the results are always the same. I’m just not sure how to go about debugging this; any help would be appreciated.