Large models lead to bad initial energy/failing chains about covid19_inference HOT 6 CLOSED

Is this due to the ode? If yes a first step to a solution could be to replace the handwritten ode solver
in

by the pymc3 internal solution from pymc3.ode, which in turn uses scipy.integrate.odeint

If you think this is sensible, I can give it a try later.

from covid19_inference.

Not really sure. I didn't know the ode module. But I don't think it is very good approach as it won't work for the SEIR-model (not yet in this repo) as it not only needs the past timestep, but up to 9 in the past.
The reason is that we implemented it such that the delay is not exponentially distrubuted, but lognormal.

from covid19_inference.

Theano has a debug mode, where it stops when nans occur, I will try that next

from covid19_inference.

I bet you a beer its the ode solver.

from covid19_inference.

Deal ;) I learnt today, that there exist a way to implement also the SEIR model with a not-exponential incubation period with an ODE (Erlang-SEIR https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673147/). So I do now think that it would be sensible to use the pymc3 ode solver for the SIR model. If you still want to do it, go on :)
I did try to avoid nans in the SIR model by clipping the infected and susceptible number between 0 and N_population, so I would think that this isn't reason for the errors.

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The error was in the weekend correction, where I used an hierarchical beta distribution. There I forgot to assure, that the sigma is in a correct range, which lead to nans. It is corrected by eb8e4e4

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