Skip to content
Archive of entries posted by

Stan Model of the Week: PK Calculation of IV and Oral Dosing

[Update: Revised given comments from Wingfeet, Andrew and germo. Thanks! I'd mistakenly translated the dlnorm priors in the first version --- amazing what a difference the priors make. I also escaped the less-than and greater-than signs in the constraints in the model so they're visible. I also updated to match the thin=2 output of JAGS.] […]

Running into a Stan Reference by Accident

We were talking about parallelizing MCMC and I came up with what I thought was a neat idea for parallelizing MCMC (sample with fractional prior, average samples on a per-draw basis). But then I realized this approach could get the right posterior mean or right posterior variance, but not both, depending on how the prior […]

Stupid R Tricks: Random Scope

Andrew and I have been discussing how we’re going to define functions in Stan for defining systems of differential equations; see our evolving ode design doc; comments welcome, of course. About Scope I mentioned to Andrew I would prefer pure lexical, static scoping, as found in languages like C++ and Java. If you’re not familiar […]

Mailing List Degree-of-Difficulty Difficulty

The Difficulty with Difficult Questions Andrew’s commented during our Stan meetings that he’s observed that when a user sends an easy question to a mailing list, it gets answered right away, whereas difficult questions often languish with no answers. These difficult questions usually come from power users with real issues, whereas the simple questions are […]

Shlemiel the Software Developer and Unknown Unknowns

The Stan meeting today reminded me of Joel Spolsky’s recasting of the Yiddish joke about Shlemiel the Painter. Joel retold it on his blog, Joel on Software, in the post Back to Basics: Shlemiel gets a job as a street painter, painting the dotted lines down the middle of the road. On the first day […]

Most Popular Girl Names by State over Time

The following should be catnip for Andrew. It combines (a) statistics on baby names, (b) time series, and (c) statistics broken down by state. All in one really fun animated visualization by Reuben Fischer-Baum: Sixty Years of the Most Popular Names for Girls As Mark Liberman commented in his re-post on Language Log, this data […]

Samplers for Big Science: emcee and BAT

Over the past few months, we’ve talked about modeling with particle physicists (Allen Caldwell), astrophysicists (David Hogg, who regularly comments here), and climate and energy usage modelers (Phil Price, who regularly posts here). Big Science Black Boxes We’ve gotten pretty much the same story from all of them: their models involve “big science” components that […]

Stan Project: Continuous Relaxations for Discrete MRFs

Hamiltonian Monte Carlo (HMC), as used by Stan, is only defined for continuous parameters. We’d love to be able to do discrete sampling. So I was excited when I saw this: Yichuan Zhang, Charles Sutton, Amos J Storkey, and Zoubin Ghahramani. 2012. Continuous Relaxations for Discrete Hamiltonian Monte Carlo. NIPS 25. Abstract: Continuous relaxations play […]

Improvements to Kindle Version of BDA3

I let Andrew know about the comments about the defective Kindle version of BDA2 and he wrote to his editor at Chapman and Hall, Rob Calver, who wrote back with this info: I can guarantee that the Kindle version of the third edition will be a substantial improvement. We publish all of our mathematics and […]

Foundation for Open Access Statistics

Now here’s a foundation I (Bob) can get behind: Foundation for Open Access Statistics (FOAS) Their mission is to “promote free software, open access publishing, and reproducible research in statistics.” To me, that’s like supporting motherhood and apple pie! FOAS spun out of and is partially designed to support the Journal of Statistical Software (aka […]