Painless, efficient, general-purpose sampling from continuous distributions.
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commit e9682dd04847450d088b0671a14982ec924d0f1f
parent c065348f360642efdee58116b9720a29d8107dce
Author: Jared Tobin <jared@jtobin.ca>
Date:   Wed,  6 Apr 2016 17:08:51 +0700


MREADME.md | 7++++---
1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md @@ -16,9 +16,10 @@ In general this sampler is useful when you want decent performance without dealing with any tuning parameters or local proposal distributions. Check out the paper describing the algorithm [here](http://msp.org/camcos/2010/5-1/camcos-v5-n1-p04-p.pdf), and a paper on -some potential limitations [here](http://arxiv.org/abs/1509.02230). There is -also also a robust Python implementation -[here](http://dan.iel.fm/emcee/current/) authored by [Dan +some potential limitations [here](http://arxiv.org/abs/1509.02230), authored +by my friends David Huijser and [Brendon +Brewer](https://www.stat.auckland.ac.nz/~brewer/). There is also also a robust +Python implementation [here](http://dan.iel.fm/emcee/current/) authored by [Dan Foreman-Mackey](http://dan.iel.fm), a very nice dude who I once moved some furniture with.