You’ve probably noticed that Deep Learning is all the rage right now. AlphaGo has beaten the world champion at Go, you can google cat photos and be sure you won’t accidentally get photos of canines, and many other near-miraculous feats: all enabled by Deep Learning with neural nets. (I am thinking of coining the phrase “laminar learning” to add some panache to old-school non-deep learning.)
I do a lot of my work in R, and it turns out that not one but two R packages have recently been released that enable R users to use the famous Python-based deep learning package,
Percolation is the ability of a liquid-like substance to get through a solid-like lattice. An interesting question is how the likelihood of a material allowing percolation changes as the average density of the lattice changes from 100% (i.e. solid with no percolation) to 0% (i.e. nothing with total percolation). Read an interesting article that looks at the case of square lattices using R: Percolation Threshold on a Square Lattice
Andrew Gelman, et al’s Bayesian Data Analysis 3rd edition is coming this Fall! The second edition was a classic, and they’ve added several chapters and polished everything nicely. I’ve already ordered my copy.
This edition won’t be as Stan-oriented as I’d have liked, but it does have two appendix sections on Stan.