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Archive of posts filed under the Stan category.

Stan’s Super Bowl prediction: Broncos 24, Panthers 13

We ran the data through our model, not just the data from the past season but from the past 17 seasons (that’s what we could easily access) with a Gaussian process model to allow team abilities to vary over time. Because we’re modeling individual game outcomes, our model automatically controls for imbalances such as Carolina’s […]

Stunning breakthrough: Using Stan to map cancer screening!

Paul Alper points me to this article, Breast Cancer Screening, Incidence, and Mortality Across US Counties, by Charles Harding, Francesco Pompei, Dmitriy Burmistrov, Gilbert Welch, Rediet Abebe, and Richard Wilson. Their substantive conclusion is there’s too much screening going on, but here I want to focus on their statistical methods: Spline methods were used to […]

Postdoc opportunity with Sophia Rabe-Hesketh and me in Berkeley!

Sophia writes: Mark Wilson, Zach Pardos and I are looking for a postdoc to work with us on a range of projects related to educational assessment and statistical modeling, such as Bayesian modeling in Stan (joint with Andrew Gelman). See here for more details. We will accept applications until February 26. The position is for […]

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 […]

Kéry and Schaub’s Bayesian Population Analysis Translated to Stan

Hiroki ITÔ (pictured) has done everyone a service in translating to Stan the example models [update: only chapters 3–8, not the whole book; the rest are in the works] from Marc Kéry and Michael Schaub (2012) Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective. Academic Press. You can find the code in our example-models repository […]

If you’re using Stata and you want to do Bayes, you should be using StataStan

Robert Grant, Daniel Furr, Bob Carpenter, and I write: Stata users have access to two easy-to-use implementations of Bayesian inference: Stata’s native bayesmh function and StataStan, which calls the general Bayesian engine Stan. We compare these on two models that are important for education research: the Rasch model and the hierarchical Rasch model. Stan (as […]

Stan Talk in NYC: Macroeconomic Forecasting using Analogy Weighting

This post is by Eric. The next Stan meetup is coming up in February. It will be hosted by the New York Bayesian Data Analysis Meetup group and International Securities Exchange. The BDA group was formerly called Stan Users – NYC. We will still be focusing on Stan, but we would also like to open it up […]

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 […]

Stan in the tabloids!

I’ve never published anything in PPNAS except for letters, but now you could say I have an indirect full publication there, as Peter Smits informs us of this new paper that uses Stan! Peter D. Smits. Expected time-invariant effects of biological traits on mammal species duration. PPNAS 2015, published ahead of print October 5, 2015, […]

Baltimore Orioles Hackathon coming soon!

Kevin Tenenbaum writes: I wanted to let you know about a hackathon that we will be hosting at Camden Yards on February 5th, 2016. This event is a great opportunity for your students to use their statistics, data science and computer science expertise to find novel solutions to problems that Major League Baseball teams deal […]

You won’t believe this story: Tamiflu conflict of interest

Paul Alper writes: Maybe it is time to return to really important things such as medical swindles in particular, Tamiflu. Consider Tamifu and its financially-influenced and influential supportors as seen from the fabulous Susan Perry of Minnpost: The group of researchers who conducted the Lancet study [supporting Tamiflu]was described in a commentary that accompanied their […]

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 […]

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 […]

Stan at NIPS 2015

With NIPS 2014 a distant memory, we have a web page covering all the Stan-related activity at NIPS 2015 Including how to score a nifty Stan sticker. If you have something else to add to the list, let us know in the comments.

Syllabus for my course on design and analysis of sample surveys

Here’s last year’s course plan. Maybe I’ll change it a bit, haven’t decided yet. The course number is Political Science 4365, and it’s also cross-listed in Statistics.

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. […]