Jonas Arruda
Jonas Arruda
I added a wrapper for variational inference with `PyMC`. The wrapper uses the `PymcSampler` as basis and directly supports all functions and methods from `PyMC` [see here for details on...
**Feature description** Depending on the context, the pypesto objective can be a likelihood or a posterior. It would be nice to have easy acess to both, e.g. if I have...
**Feature description** - Include option to stop sampling if convergence is already achieved. - Enable the option to continue sampling where the procedure stopped (e.g. give the sampler an sampling...
**Feature description** When performing hierarchical approximation using splines as the observable mapping one should include spline knots and values for each observable in the `result.optimize_result` object. **Motivation/Application** When using non-linear...
I have included a brief tutorial on how to utilize an SBML model with BayesFlow (the SBML format is very often used in computational systems biology). In the other tutoirals,...
The `NegLogParameterPriors` objective does not properly account for fixed parameters when invoked during sampling. When it's integrated into the `AggregatedObjective`, optimization functions correctly; however, issues arise during sampling because `call_unprocessed`...
The Petab importer also generates an Amici model, which requires adapting parameter scales. However, a small bug occurred due to passing the entire dictionary to the rescale function instead of...
**Bug description** The functions available in `pypesto.visualize.model_fit` cannot work with an `AggregatedObjective` and hence fail. There are two reasons: - the amici model is not available by objective.amici_model (I already...
Some minor bug fixes. These should be corrected in the main version too.
The changes include normalizing references when necessary, using the correct references when bootstrap is enabled and fixing some typos in docstrings. Added new test cases `test_references` and `test_references_norm` to validate...