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

junpenglao October 1, 2019, 1:40pm 4

You are welcome! I think if you tune for a really long time it should be able to find it but

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