On: Doing Bayesian Data Analysis On the GPU: Examples

[marmaduke]

It's not really comparable to stan if you're not computing gradients for HMC. Of course maybe for these models you don't need to.

[dragandj → marmaduke]

Please point me to models that absolutely need HMC, and I can try to see how Bayadera fares.

[marmaduke → dragandj]

Stan 's developers in particular use it for hierarchical models, but it general anything with highly correlated parameters works better with HMC than MCMC, IIRC.

Michael Betancourt (à stan dev working on the HMC parts) has a pair of YouTube videos which go into details.

That said, I switched to pymc3 so that I could compute logp via opencl more easily and if there were better ways to do this, I'm happy to see them.

[dragandj → marmaduke]

But there ARE hierarchical models in the examples. One is 158-dimensional. With highly correlated parameters. Works like a charm in Bayadera.

[marmaduke → dragandj]

My point was mainly that comparing speed between an algorithm that doesn't require gradients and HMC in Stan is apples and oranges.

[dragandj → marmaduke]

How's that? The algorithms have the same goal - find the posterior distribution. The time to get to that distribution is what is important and what is compared, provided that both algorithms get proper results. How they do it underneath is irrelevant for the user who waits.

That's like saying that comparing a horse cart and an automobile is comparing apples and oranges.

That being said, there are other things where Stan might fare better. User familiarity, or maturity, or personal taste...