Hello. I'm the person that was linked to in that GitHub issue!
I sympathize with the post's frustration. The TF tutorials on the official website are well-written. But they mostly cover basic features, and as a recent Reddit thread described (https://old.reddit.com/r/MachineLearning/comments/e4pxqp/d_i...), the support ecosystem is lacking as StackOverflow and blog posts are out-of-date due to all the software churning. I'm not a TF engineer, but as someone with experience designing libraries on top of TF, even I find myself sifting through Stack Overflow/blog post code to find the new best practices..
Regarding Bayesian layers, it's actually a NeurIPS paper this year (https://papers.nips.cc/paper/9607-bayesian-layers-a-module-f...). I worked on an early prototype in TensorFlow Probability but ended up abandoning the design as I found it inflexible in practice. The solution is the NeurIPS paper, and it's experimental: there are no promises of stability (in fact, we even moved the code from Tensor2Tensor to another repository (https://github.com/google/edward2/), of which has yet to have an official package release!).
Software for uncertainty models is more on the research fringe, and this should be made clearer in official TensorFlow solutions building on these designs.
Hi, stan dev here. I think viewing Stan as a better BUGS is helpful but limiting.
The syntax is similar, but the class of models Stan fits is far more general. The class of algorithms we have available also goes beyond MCMC, e.g., variational inference, optimization, and interfaces to Stan exist on all primary programming languages. It's more helpful to think of Stan as its own probabilistic programming language, and arguably the biggest entity with the largest user base.
>the class of models Stan fits is far more general.
This is mostly true, but last time I checked, Stan still couldn't sample discrete variables like BUGS can. Stan can only fit models with discrete parameters (e.g. finite mixtures) if the programmer is smart enough to integrate them out.
To clarify, it has been studied in Zoubin Ghahramani's group [1] (and also more recently in Ryan Adam's group [2]), and it's most widely known through Radford Neal [3] who's won a lot of competitions using the Bayesian approach to NNs.
This is not completely accurate. Deep learning at the moment simply means the use of "deep" architectures in neural networks. Graphical models, standard Bayesian hierarchical models, and the likes all form hierarchies of features as well and are commonly practiced.
I sympathize with the post's frustration. The TF tutorials on the official website are well-written. But they mostly cover basic features, and as a recent Reddit thread described (https://old.reddit.com/r/MachineLearning/comments/e4pxqp/d_i...), the support ecosystem is lacking as StackOverflow and blog posts are out-of-date due to all the software churning. I'm not a TF engineer, but as someone with experience designing libraries on top of TF, even I find myself sifting through Stack Overflow/blog post code to find the new best practices..
Regarding Bayesian layers, it's actually a NeurIPS paper this year (https://papers.nips.cc/paper/9607-bayesian-layers-a-module-f...). I worked on an early prototype in TensorFlow Probability but ended up abandoning the design as I found it inflexible in practice. The solution is the NeurIPS paper, and it's experimental: there are no promises of stability (in fact, we even moved the code from Tensor2Tensor to another repository (https://github.com/google/edward2/), of which has yet to have an official package release!).
Software for uncertainty models is more on the research fringe, and this should be made clearer in official TensorFlow solutions building on these designs.