What would the right way to model a Multivariate Normal distribution be when on every observation some values are missing, ie., I have a very sparse input. Note that we For example, my observations may look something like [[1.11, --, 0.9, --, 1.23, --], [--, 1.2, 0.81, --, --, --], [--, --, --…

Oh it’s much simpler than that. In PyMC3, potential are things you want to add to the model logp. For example, if you write with pm.Model() as m1: ... obs = pm.MvNormal('obs', mu, cov, observed=Xdata) What pymc3 does within the model m1 is that the logp of obs will be summed along with oth…

Multivariate Normal with missing inputs

Questions

aayushy December 27, 2018, 8:12am 10

You’re right, I was thinking of something else (which was, in fact, incorrect).

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Multivariate Normal with missing inputs

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