Kind regards,

I am building a model in which each observation is a matrix, i want to model it using the matrix normal distribution as define in:

Thanks.

Kind regards,

I am building a model in which each observation is a matrix, i want to model it using the matrix normal distribution as define in:

Thanks.

No. Use the third equation on the wikipedia page that you linked to generate it yourself. You’ll have to specify priors for both the row and column covariance matrices – LKJ priors for correlations and [whatever you want] for volatilities will do nicely – then Kronecker product them together to get the overall covariance matrix for the multivariate normal. A prior for the mean is [whatever you want], just in vector form. After you draw from `MultivariateNormal`

, reshape into the matrix shape you want. Does that make sense?

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