Can I make out-of-sample predictions using posterior means? Or how to use scipy.optimize with pymc predictions?

One could use the mean if the optimizer was already on the graph informing that mean, but not otherwise

I have no idea what an optimizer inside a graph informing a mean means.

What I was saying is if you have, say, a LogNormal observational model and want to optimize the expected value you can optimize the “analytical mean”/first moment of the distribution from the posterior draws of the parameters of that LogNormal and whatever parameters you want to tune.

You don’t need to draw a bunch of posterior predictive draws to then take its mean.

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