Elba Raimúndez

Storage of intermediate results

14

**Feature description** Allow pyPESTO to store intermediate results before the whole process is finished (e.g. optimization, sampling). **Motivation/Application** This is important specially when working with more computationally demanding models, e.g....

Adaptive metropolis sampler uses implicitly a uniform prior

8

In the following line of code, the sample is checked to be within the parameter bounds used for optimization https://github.com/ICB-DCM/pyPESTO/blob/master/pypesto/sampling/metropolis.py#L59 this is how it was implemented in the MATLAB code....

importer.create_objective() should return the negative log posterior or just the neg. log likelihood ?

4

I am using a model containing PEtab priors. Therefore my `problem.objective` is an aggregated objective containing negative log likelihood + negative log prior. At the moment, calling `importer.create_objective()` returns the...

What should a empty prior return?

5

In the case where parameter priors are not defined in a optimization problem currently `problem.x_priors` returns `None`. To keep things simple (and integrate in the MCMC sampling workflow), I think...

Obtain correct parameter scale from petab import in parameter names

2

When petab problem is imported in pypesto would be good to update parameter names according to the scale: `lin`, `log` or `log10`. In case being `lin` leave as it is....

Dear d2d team, I am currently trying to export a d2d model into the PEtab format by using `arExportPEtab('model_name', true)`. This model has 4 different preequilibration conditions, ``` >>> length(ar.model(1).ss_condition)...

For model selection https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004784