Seth Axen

e.g., using the `idata` created in the Quickstart: ```julia plot_dist_comparison(idata; var_names=[:μ, :τ]) ```  ```julia plot_dist_comparison(idata; coords=Dict(:school => ["Choate", "Deerfield"]), kind="observed") ``` 

> give a warning or something when a user wants to use `plot_dist_comparison` on inference data that has no prior Good suggestion! That should probably be handled by the underlying...

Actually, the prior doesn't seem to be required to use `plot_dist_comparison` e.g. ```julia julia> using ArviZ julia> idata = load_arviz_data("centered_eight") InferenceData with groups: > posterior > posterior_predictive > sample_stats >...

Apparently [PythonCall](https://github.com/cjdoris/PythonCall.jl) allows for providing a TOML file for dependencies to ensure reproducibility, so that's an alternative. I'm not certain how well PyPlot will work without PyPlot.jl, which uses PyCall...

I spent some time replacing PyCall with PythonCall, but it turns out to require quite a lot of surgery. PythonCall by default does no conversions between Python and Julia, and...

Thanks, @torfjelde! I love that these changes both are more general and simplify the code quite a bit. Is it possible, given a `DynamicPPL.Model`, to determine what in its arguments...

> You could just call `pointwise_loglikelihoods` once and then you have them as keys:) > > Actually, did you figure this out on your own? Looking at `_compute_log_likelihood` it looks...

@benmwebb: >I don't think it's a good idea to have the scoring function examine all particles in the model looking for Nuisances (if I understand you correctly). ... The simplest...

Here's a better alternative: Implement a child of `Nuisance` called `TransformedNuisance`. It should be usable everywhere where `Nuisance` is used, but the value of the nuisance is constrained to be...

All the pieces are there for uni- and multi-variate adjustment of score and gradients. Without modifying `IMP.ScoringFunction`, the last piece is for `IMP.core.JacobianAdjuster.get_score_adjustment()` to be called everywhere in an `IMP.Optimizer`...