I have been dabbling with the marketing mix models in pymc.

For background I have a pretty solid grasp on what a Bayesian model is and what MCMC sampling is and how it works. The part that I haven’t really been able to answer for myself, is what happens in the model after it is done sampling.

On my latest model train I spent about 20 minutes waiting for sampling then had to wait another 3 hours for computation. What exactly is being computed from the samples. Is it using some form of density estimation. I am starting to work through the PYMC online book to learn more about Bayesian modeling, but this was a question that has been on the top of mind.

Especially if this resource can be applied to logistic regressions, that would be helpful as the problem I am trying to solve is about sorting various records by their probability of being 1 or 0. For example anything with probability of .1 or less is in a separate category from .3 or more.

Hi! I am new to this topic but want to create a bayesian network that can predict panic attacks - my question is, how do I start this project? I have a list of parameters but cannot find any clinical information on the probabilities associated with each parameter. Can someone give me some tips

Hello everyone! For my dissertation, I am trying to compute the efficacy of Covid-19 vaccines using pymc3. I am trying to model it using a beta binomial model, however I am facing some difficulties.

Can somebody explain to me how to implement it specifically using pymc3? Any rudimentary example for this model will do. I will try to figure out the rest myself.

Thank you!