3 ms·
> I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great to
by elcritch 6y ago
> I fall heavily on the Pearl side of this debate, because I'm interested in scaling to thousands to millions of variables, and though graphs are not a great tool, they are pretty much the only one that I think we have for scaling that direction.
Are you able to elaborate?! It'd be really interesting to hear what/if tooling exists for these graphs. I just found the book recently and am listening to the Audible version. It also reaffirms my desire for a "Bayesian inference" spreadsheet like tool. Something that'd help organize a few dozen thoughts ideas for researchers/engineers.
- currymj 6y agoR package "daggity" is excellent. http://www.dagitty.net/ http://www.dagitty.net/
- epistasis 6y agoI've used libDAI in the past, but I'm not sure if it's maintained anymore. I've come across several other toolkits that looked interesting since then, but never had the right project. There's a group at (CMU?)/Pitt that I think now has a Python package for causal inference in particular. (Causality is far more complicated and harder to discover than a lot of the stuff I work towards though, which is all in biology.)
- canjobear 6y ago> a "Bayesian inference" spreadsheet like tool http://probcomp.csail.mit.edu/software/bayesdb/ http://probcomp.csail.mit.edu/software/bayesdb/
- sjg007 6y agoThousands and millions of variables approaches deep learning w/ softmax. It is difficult to see how it would work any other way. I mean would you want to have a causal model of particles?