Substantial updates to tutorial 01_gaussian-mixture-model

[JasonPekos]

First PR! Hope everything is ok.

As discussed in the slack, this PR adds the following significant changes to this tutorial

• [x] Covers the use of ordered() from Bijectors.jl in making the model identifiable (currently it is multimodal, and the seed is just lucky.)
• [x] Introduces a (MUCH faster) version of the model with assignments marginalized out with Turing.@addlogprob!
• [x] A very simple version of the marginalized model using only ~ MixtureModel(dists, weights)
• [x] Example of recovering marginalized assignment draws with generated_quantities()

There are also a few minor changes:

• [x] Changed chain plots from grouping chains together to grouping parameters together. I think this makes the multimodality discussion much clearer.
• [x] Added a tiny amount of burnin to the MCMC sampler, as really far-out initialization was making the plots hard to read
• [x] Added one more chain to n-chains to make multimodality more likely
• [x] Added and reworked tests for the models (now tests rhat to make sure the bijector discussions are all correct)

[JasonPekos]

I think maybe this should be closed and revisited when https://github.com/TuringLang/TuringTutorials/pull/441 is done?

fwiw the current thing that's keeping this frozen is the multithreading stuff. If we want to stay away from:

chains = sample(model, sampler, MCMCThreads(), nsamples, nchains; discard_initial = burn);

to avoid warnings in single threaded environments, we'll need to update a bunch of tutorials, because this is pretty common across all the tutorials.

[yebai]

Thanks, @JasonPekos, for the PR. Would you like to migrate your changes here to #441?

[JasonPekos]

Thanks, @JasonPekos, for the PR. Would you like to migrate your changes here to #441?

Yup, will do.