Use inverse matrix gamma distribution as prior for covariance matrix of multivariate normal (in Python)

Hi, I’m trying to reimplement the Bayesian model from this paper. They mention in the Supplemental Information that they assume a multivariate prior on the weights — I know how to deal with the mean vector, but they say that “The covariance matrix is defined by an Inverse-Gamma distribution with the two hyperparameters (a, b). The simulation sets the initial values of the two hyperparameters as (a0 = 1, b0 = 5).” I’m trying to do this in PyMC3, and I don’t see how to define the covariance matrix with this distribution (is the inverse-wishart really what I want?)? I would also give PyStan a shot if someone knew how to do this there. This is my first foray into Bayesian modeling, so any help would be hugely appreciated. submitted by /u/squirreltalk [link] [comments]

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Nevin Manimala

Nevin Manimala is interested in blogging and finding new blogs

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