[chmullig]

Interesting. They talk about plain old Metropolis Hastings, which is pretty questionable.

Anyone excited about this, I highly recommend checking out Stan; it's under active development, actually works with real problems, and is used in the real world. With NUTS and HMC they've really made good on their promises, and quite soon they'll have meaningful ADVI support. See this former discussion: https://news.ycombinator.com/item?id=10244771

[mwexler]

Stan (http://mc-stan.org/) is impressive, but isn't this BLOG language easier to read and perhaps easier for novices to create models in? The marriage of the power of Stan with the ease and speed of implementation of BLOG could create the next generation of probablistically driven experiences by opening up the power to more, and that would be a cool thing.

[alkalait]

I'm a bit familiar with PyMC, but all it seems to do is Gibbs sampling, which mixes horribly compared to HMC.

How easy would the transition to PyStan be?

[ced]

Not trivial, but there's a Python wrapper available:

https://github.com/stan-dev/pystan

[data_scientist]

PyMC 3 implements HMC. It is still in beta but quite stable

[twiecki]

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 for free so that HMC and NUTS can be used which work models of high complexity.

I use it in production, despite it still being beta. We're close to the first stable release but there are still some small kinks to figure out.

Disclaimer: I'm a co-developer.

[rfergie]

Probably pretty easy.

It is different, but the core semantics are the same so you just have to worry about new syntax (and worse python integration)