Cameron Pfiffer

That's got my full support! Damn I hate Jupyter notebooks.

Strange, doesn't seem to be yielding good inference: 

And I am now getting this: ```julia MethodError: Cannot `convert` an object of type Array{Float64,1} to an object of type SubArray{Float64,1,Array{Float64,1},Tuple{UnitRange{Int64}},true} Closest candidates are: convert(::Type{T}, !Matched::T) where T

All this is in here: https://github.com/TuringLang/TuringTutorials/blob/master/3_BayesNN.ipynb No tutorial has been successfully served on the site with the VI code in it. Is it worth fixing or should I just remove...

Something I've always wondered about this is why we don't just track these generated quantities in `VarInfo`? It seems like it would be reasonably easy to build a `VarName` from...

I wonder if we can just do away entirely with `generated_quantities`. I'd prefer to handle everything at sampling time rather than ex-post, which seems really clumsy to me. For example,...

I think those are all great points, particularly the idea about using different models. The memory constraints thing is not that big an issue though, since I was also thinking...

I would like to see this a lot too -- I could imagine cases where someone had a distribution across [bigrams](https://en.wikipedia.org/wiki/Bigram) or something and just wanted to work with the...

We could actually probably just write this an an extension of `bundle_samples` inside Turing.jl and treat it as a chain type, so you could just call ```julia chain = sample(model,...

I actually kind of like the Tables-style interface you proposed. As to returning a `NamedTuple` from `eachsample`, I'm not sure -- my preference would be to return the `Transition` given...