The "Bayesian Differential Equation" tutorial needs small improvements

[nathanaelbosch]

From: https://github.com/TuringLang/Turing.jl/edit/master/docs/_tutorials/10_BayesianDiffEq.md

The tutorial results seem to have changed since its initial writing. For example, the plot in the Data retrodiction section does not reproduce the true ODE solution "quite accurately", since there is a big mismatch between the parameters in the three chains. I suspect that to resolve this issue and fix the example, some minor parameter tweaking could already be sufficient.

[devmotion]

Yes, the results should definitely be improved or one has to weaken this claim 😄

Currently, the tutorials are reorganized and moved to Julia markdown files that are easier to handle than Jupyter notebooks. You can follow the progress here: #113 It should be done soon and be based on more recent versions of Turing, MCMCChains etc. Hopefully the new structure makes it easier to keep the package versions updated and fix issues.

[devmotion]

The tutorial was just updated yesterday (#296) and runs fine with the latest versions of Turing and DifferentialEquations (see https://turing.ml/dev/tutorials/10-bayesian-differential-equations/). It's part of our CI pipeline, and hence it should be easy to spot and fix regressions when e.g. updating Turing or DifferentialEquations in the future.