Comments (1)
After thinking about this for a while, I don't think it's necessary to implement this in chi. While occasionally it may be nice to look at pooled covariate models the current hierarchical modelling design requires that all individual parameters are exposed or only the pooled parameters are exposed and the individual parameters are referenced to this pooled parameter. Additional transformations are currently not supported and it might make the hierarchical log-likelihood even more complicated than it already is. As a workaround in the rare cases where one would like to use a pooled covariate population model, one can instantiate the regular covariate population model and fix all etas and sigma. This will lead to a unnecessary posterior contribution which is however constant and therefore does not influence the inference results.
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Related Issues (20)
- Make sure that renaming didn't break anything
- Create tutorial on how to simulate a model
- update to latest pints release
- prepare for release
- Better naming of data columns
- Support centered parametrisation of LogNormalLinearCovariateModel
- Bug MechanisticModel parameters: derived constants are not filtered
- Sampling controller and sampling from CovariatePopulationModels is incompatible
- Bug HierarchicalLogPosterior.evaluateS1 seems buggy for CovariateModels HOT 1
- Replace pd.DataFrame.append by pd.concat
- Bug: PMC fix population parameters before setting data
- Unwanted behaviour: HLL.call
- Deprecate PharmacokineticModel and PharmacodynamicModel
- fix bug: chi.MechanisticModel miscounts number of parameters in rare SBML model
- Implement a ComposedPopulationModel
- population filter
- Fix minor bugs
- Bug: hierarchicalLL sensitivities when population parameters are fixed
- Upgrade myokit version
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