Viewing profile — twiecki

There is a collection of curated resources here: https://www.pymc.io/projects/docs/en/stable/learn.html

If you are looking for an explanation of MCMC that focuses on intuitive understanding to complement more mathematical introductions, I wrote a blog post trying to explain things in…

No, back-propagation would not give full Bayesian inference (although there are some tricks [0]). They instead use variational inference[1], which allows for fast inference of cont…

Brian Moriarty gave a great talk in 2015 about his story of Loom with many interesting behind-the-scenes infos: https://www.youtube.com/watch?v=z1aVDael-KM Towards the end he also …

There are packages that are just Python libraries, like PyMC3: http://pymc-devs.github.io/pymc3/ It allows you to specify a Probabilistic Program with a few lines of Python code (x…

I don't think it has to be either or. For example, you can implement a Deep Net in a probabilistic programming framework ( http://twiecki.github.io/blog/2016/07/05/bayesian-deep-le…

PyMC3 ( http://pymc-devs.github.io/pymc3/ ) has all the powerful samplers that Stan has (i.e. NUTS) as well as support for discrete priors. It allows you to specify your models in …

Thanks for your comment! I did split the data in two, but it's easy to miss. X_test and X_train are the two sets. ann_input.set_value(X_test) then switches in the test values. That…

If you're interested: http://pymc-devs.github.io/pymc3/ PyMC3 uses Theano to create a compute graph of the model which then gets compiled to C. Moreover, it gives us the gradient f…

Anyone else notice that the code wasn't PEP8 compatible? ;) I went ahead and fixed it: https://gist.github.com/twiecki/7361de09b8e4c0a6248e

The book is based on PyMC2, a Python module for Bayesian inference: http://pymc-devs.github.io/pymc/ PyMC3 is nearing the first beta release and is a complete rewrite that's using …