Add TransformedDataset class in order to enable inference for stochastic states

[cweniger]

Goal: Strategy for generating posteriors for parameters with implicit priors.

• Parameters are part of the model prediction
• Original priors and simulations are defined as before
• Dataset is the thing that provides information about priors, bounds and parameter names
  • We should tweak the dataset to change the expected results
• We could have some transformed dataset?
  • Overwrite: pnames, v, bound, prior
  • The prior would have to be defined empirically, based on the distribution of the parameter in the training set.

Suggestion: TransformedDataset(dataset, sim_to_v, pnames, eff_prior, eff_bound)

[cweniger]

In v0.4 everything is based on implicit priors by construction. Closed