Work in progress. leave this PR open

[fccoelho]

Added two new inference runs: One with the full SIR and another with the 1D.

[maxbiostat]

Comments:

• The Cauchy prior on the log-normal variance (likelihood) might not be the best choice here. It might create poor posterior geometry at no gain on the modelling side. Would a simpler standard half normal or even a Gamma(1, 1) prior work OK here?
• The PyMC3 HMC sampler is struggling to lock onto and maintain a target acceptance probability. We need to check why that is;
• Would it be possible to mark the generating ("true") parameter values in the posterior plots so we have a way to gauge parameter recovery?
• Would be nice to implement predictions early on, so we can compare the posterior predictive to the observed data.

[fccoelho]

• We can explore other priors for the log-normal variances But I think we can do that on another PR
• The issue the the acceptance probability may be resolved by increasing the number of burnin samples, I force down the number of samples to get some quick and dirty first estimates.
• Yes it is possible to improve the posterior plots, I'll do that next.
• Agreed. Also like to always compare posterior curves with data. It's not hard to do.

[fccoelho]

I think we can merge this PR for now, @maxbiostat .