Bayesian Logic Programming

Post by ced » Fri, Dec 18, 2015, 2:25 PM UTC

alkalait wrote: post #13

chmullig wrote: post #6

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.…

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?

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

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

Post by twiecki » Fri, Dec 18, 2015, 2:28 PM UTC

data_scientist wrote: post #19

alkalait wrote: post #13

Earlier quoted context omitted.

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?

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

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.

Re: Bayesian Logic Programming

Re: Bayesian Logic Programming