Are those hierarchical models? I also suppose the variables are continuous? What are typical running times for your analysis with Anglican, and what with PyMC?
On Sunday, October 23, 2016 at 8:17:16 PM UTC+2, Boris V. Schmid wrote: > > I am using Anglican for estimating parameters of epidemiological models, > generally in the shape of limited (mortality) data, and less than a dozen > parameters that need to be simultaneously estimated. Works fine for that. A > good example of that type of problem is here: > http://www.smallperturbation.com/epidemic-with-real-data (but with PyMC, > a similar package for python). > > But you might be right that it won't hold in high-dimensional problems. > People in genomics are running models with many thousands of parameters > when trying to figure out how different genes contribute to a particular > cell phenotype. Don't think I would try that in Anglican :-). > > > On Sunday, October 23, 2016 at 6:06:49 PM UTC+2, Dragan Djuric wrote: >> >> Thanks. I know about Anglican, but it is not even in the same category, >> other than being Bayesian. Anglican also has MCMC, but, looking at the >> implementation, it seems it is useful only on smaller problems with >> straightforward and low-dimensional basic distributions, or discrete >> problems/distributions. I do not see how it can be used to solve even >> standard textbook examples in "real" bayesian data analysis. Otherwise, I'd >> use/improve Anglican, although its GPL license is a bit of a showstopper. >> >> I would loved to have been able to see how far Anglican can go >> performance-wise, and stretch it to its limits, though. However, it wasn't >> obvious how to construct any of more serious data analysis problems. Having >> seen its implementation, I expect the performance comparison would make >> Bayadera shine, so I hope I'll be able to construct some examples that can >> be implemented in both environments :) >> >> On Sunday, October 23, 2016 at 3:47:50 PM UTC+2, Boris V. Schmid wrote: >>> >>> Thanks Dragan. >>> >>> Interesting slides, and interesting section on Bayadera. Incanter, as >>> far as I know indeed doesn't support MCMC, but there is a fairly large >>> project based on clojure that does a lot of bayesian inference. >>> >>> Just in case you haven't run into it: >>> http://www.robots.ox.ac.uk/~fwood/anglican/examples/index.html >>> >>> (for the far future, there are some interesting developments happening >>> with approximate bayesian inference using neural network classification to >>> speed things up. Fun stuff.) >>> >>> On Thursday, October 20, 2016 at 11:38:25 PM UTC+2, Dragan Djuric wrote: >>>> >>>> Hi all, I posted slides for my upcoming EuroClojure talk, so you can >>>> enjoy the talk without having to take notes: >>>> http://dragan.rocks/articles/16/Clojure-is-not-afraid-of-the-GPU-slides-EuroClojure >>>> >>> -- You received this message because you are subscribed to the Google Groups "Clojure" group. To post to this group, send email to clojure@googlegroups.com Note that posts from new members are moderated - please be patient with your first post. To unsubscribe from this group, send email to clojure+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/clojure?hl=en --- You received this message because you are subscribed to the Google Groups "Clojure" group. To unsubscribe from this group and stop receiving emails from it, send an email to clojure+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
