Let’s say we have a matrix: [M = \begin{bmatrix} \mu_{11} & \mu_{12} & \dots & \mu_{1m} \\ \mu_{21} & \mu_{22} & \dots & \mu_{2m} \\ \hdotsfor{4} \\ \mu_{n1} & \mu_{n2} & \dots & \mu_{nm} \end{bmatrix}] and a matrix: [\Sigma = \begin{bmatrix} \sigma_{11} …

If the MvNormal is a latent variable, it is much more straightforward to reparameterized it as Y = mu + L.dot(s) with s ~ Normal(0, 1) n, m = 100, 5 Mean = np.random.randn(m, n)*5 with pm.Model() as model: sd_dist = pm.HalfNormal.dist(2.5) packed_chol = pm.LKJCholeskyCov('chol', n=m, eta=2…

About set of Multivariate Normal Distributions

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Kyrish July 3, 2018, 9:58am 3

Thanks! The second solution is exactly what I need.

Best Regards
Alex

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About set of Multivariate Normal Distributions

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