Hi all, is there a straightforward way to do the following: I have a pymc3.model object, and I would like to evaluate pymc3.model.logp for given sample from the prior distributions over wich is my pymc3.model defined. Something along the lines pymc3_model.logp(prior_sample, transform = None). I …

You can pass the prior_sample as a dict into pymc3_model.logp. There is no option to pass transform = None which means you need to always provide a dictionary of RVs after they are transformed. However, you can use the pm.util.update_start_vals to update the dictionary: pm.util.update_start_vals(pr…

Pymc3.model likelihood evaluation for given prior sample

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kejzlarv June 22, 2018, 10:16pm 3

That works great, thanks!

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Pymc3.model likelihood evaluation for given prior sample

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