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.
