I want to model a matrix as a multivariate normal with univariate normal priors on the column means and an LKJ prior on the covariance matrix. Something like this works fine, as expected: df = (df - df.mean()) / df.std() # necessary! m = len(df.columns) mu = pm.Normal("mu", 0, sd=1, shape=m) sd_di…

Using multivariate normal with widely varying means

sammosummo October 1, 2019, 1:17pm 3

That actually helped a lot, I’m embarrassed not to have thought of it. Thanks!

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