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Archive of posts filed under the Statistical computing category.

TOP SECRET: Newly declassified documents on evaluating models based on predictive accuracy

We recently had an email discussion among the Stan team regarding the use of predictive accuracy in evaluating computing algorithms. I thought this could be of general interest so I’m sharing it here. It started when Bob said he’d been at a meting on probabilistic programming where there was confusion on evaluation. In particular, some […]

One quick tip for building trust in missing-data imputations?

Peter Liberman writes: I’m working on a paper that, in the absence of a single survey that measured the required combination of variables, analyzes data collected by separate, uncoordinated Knowledge Networks surveys in 2003. My co-author (a social psychologist who commissioned one of the surveys) and I obtained from KN unique id numbers for all […]

Jim Albert’s Baseball Blog

Jim Albert has a baseball blog: Baseball with R I sent a link internally to people I knew were into baseball, to which Andrew replied, “I agree that it’s cool that he doesn’t just talk, he has code.” (No kidding—the latest post as of writing this was on an R package to compute value above […]

McElreath’s Statistical Rethinking: A Bayesian Course with Examples in R and Stan

We’re not even halfway through with January, but the new year’s already rung in a new book with lots of Stan content: Richard McElreath (2016) Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman & Hall/CRC Press. This one got a thumbs up from the Stan team members who’ve read it, and […]

rstanarm and more!

Ben Goodrich writes: The rstanarm R package, which has been mentioned several times on stan-users, is now available in binary form on CRAN mirrors (unless you are using an old version of R and / or an old version of OSX). It is an R package that comes with a few precompiled Stan models — […]

Stan 2.9 is Here!

We’re happy to announce that Stan 2.9.0 is fully available(1) for CmdStan, RStan, and PyStan — it should also work for Stan.jl (Julia), MatlabStan, and StataStan. As usual, you can find everything you need on the Stan Home Page. The main new features are: R/MATLAB-like slicing of matrices. There’s a new chapter in the user’s […]

Showdown in Vegas: When the numbers differ in the third decimal place

From the Stan users list: I have just started to look into the output of the optimizing function and it seems to give estimates slightly different than the ones that I had previously obtained through maximum likelihood estimation (using MATLAB). Can you please tell me what is the penatly that the LBFGS algorithm imposes? In […]

R sucks

I’m doing an analysis and one of the objects I’m working on is a multidimensional array called “attitude.” I took a quick look: > dim(attitude) [1] 30 7 Huh? It’s not supposed to be 30 x 7. Whassup? I search through my scripts for a “attitude” but all I find is the three-dimensional array. Where […]

Working Stiff

After a challenging development process we are happy to announce that Stan finally supports stiff ODE systems, removing one of the key obstacles in fields such as pharmacometrics and ecology.  For the experts, we’ve incorporated CVODE 2.8.2 into Stan and exposed the backward-differentiation formula solver using Newton iterations and a banded Jacobian computed exactly using our autodiff. […]

My talks at Nips

Today (Fri 11 Dec 2005), 4:30pm, room 514a, The Statistical Crisis in Science, in Workshop on Adaptive Data Analysis Today, 4:55pm, room 513ab, on a panel in Workshop on Advances in Approximate Bayesian Inference Tomorrow (Sat), 9am, room 513ab, Adventures on the Efficient Frontier, in Workshop on Scalable Monte Carlo Also see here.

Why I decided not to enter the $100,000 global warming time-series challenge

tl;dr: Negative expected return. Long version: I received the following email the other day from Tom Daula: Interesting applied project for your students, or as a warning for decisions under uncertainty / statistical significance. Real money on the line so the length of time and number of entries required to get a winner may be […]

Probabilistic Integration

Mark Girolami sends along a new paper by Francois-Xavier Briol, Chris Oates, Michael Osborne, Dino Sejdinovic, and himself. The idea is to consider numerical integration as a statistical problem, to say that the integral being estimated is an unknown parameter and then to perform inference about it. This is related to ideas of Xiao-Li Meng, […]

Boston Stan meetup 1 Dec

Here’s the announcement: Using Stan for variational inference, plus a couple lightning talks Dustin Tran will give a talk on using Stan for variational inference, then we’ll have a couple lightening (5 minute-ish) talks on projects. David Sparks will talk, I will talk about some of my work and we’re looking for 1-2 more volunteers. […]

Flatten your abs with this new statistical approach to quadrature

Philipp Hennig, Michael Osborne, and Mark Girolami write: We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. . . . We describe how several seminal classic numerical methods can be interpreted naturally as probabilistic inference. […]

Stan Puzzle 2: Distance Matrix Parameters

This puzzle comes in three parts. There are some hints at the end. Part I: Constrained Parameter Definition Define a Stan program with a transformed matrix parameter d that is constrained to be a K by K distance matrix. Recall that a distance matrix must satisfy the definition of a metric for all i, j: […]

Pareto smoothed importance sampling and infinite variance (2nd ed)

This post is by Aki Last week Xi’an blogged about an arXiv paper by Chatterjee and Diaconis which considers the proper sample size in an importance sampling setting with infinite variance. I commented Xi’an’s posting and the end result was my guest blog posting in Xi’an’s og. I made an additional figure below to summarise […]

Bayesian Computing: Adventures on the Efficient Frontier

That’s the title of my forthcoming talk at the Nips workshop at 9am on 12 Dec.

This is a workshop you can’t miss: DataMeetsViz

This looks like it was a great conference with an all-star lineup of speakers. You can click through and see the talks.

4 for 4.0 — The Latest JAGS

This post is by Bob Carpenter. I just saw over on Martyn Plummer’s JAGS News blog that JAGS 4.0 is out. Martyn provided a series of blog posts highlighting the new features: 1. Reproducibility: Examples will now be fully reproducible draw-for-draw and chain-for-chain with the same seed. (Of course, compiler, optimization level, platform, CPU, and […]

2 new thoughts on Cauchy priors for logistic regression coefficients

Aki noticed this paper, On the Use of Cauchy Prior Distributions for Bayesian Logistic Regression, by Joyee Ghosh, Yingbo Li, and Robin Mitra, which begins: In logistic regression, separation occurs when a linear combination of the predictors can perfectly classify part or all of the observations in the sample, and as a result, finite maximum […]