Is it possible to incrementally update a model in pyMC3. I can currently find no information on this. All documentation is always working with a priori known data.
But in my understanding, a Bayesian model also means being able to update a belief. Is this possible in pyMC3? Where can I find info in this?
Thank you :)
Following @ChrisFonnesbeck's advice, I wrote a small tutorial notebook about incremental prior updating. It can be found here:
https://github.com/pymc-devs/pymc3/blob/master/docs/source/notebooks/updating_priors.ipynb
Basically, you need to wrap your posterior samples in a custom Continuous class that computes the KDE from them. The following code does just that:
Then you define the prior of your model parameter (say
alpha
) by calling thefrom_posterior
function with the parameter name and the trace samples from the posterior of the previous iteration: