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Archive of posts filed under the Stan category.

“The problem of infra-marginality in outcome tests for discrimination”

Camelia Simoiu, Sam Corbett-Davies, and Sharad Goel write: Outcome tests are a popular method for detecting bias in lending, hiring, and policing decisions. These tests operate by comparing the success rate of decisions across groups. For example, if loans made to minority applicants are observed to be repaid more often than loans made to whites, […]

Bob’s talk at Berkeley, Thursday 22 March, 3 pm

It’s at the Institute for Data Science at Berkeley. Hierarchical Modeling in Stan for Pooling, Prediction, and Multiple Comparisons 22 March 2018, 3pm 190 Doe Library. UC Berkeley. And here’s the abstract: I’ll provide an end-to-end example of using R and Stan to carry out full Bayesian inference for a simple set of repeated binary […]

What prior to use for item-response parameters?

Joshua Pritkin writes: There is a Stan case study by Daniel Furr on a hierarchical two-parameter logistic item response model. My question is whether to model the covariance between log alpha and beta parameters. I asked Daniel Furr about this and he said, “The argument I would make for modelling the covariance is that it […]

Research project in London and Chicago to develop and fit hierarchical models for development economics in Stan!

Rachael Meager at the London School of Economics and Dean Karlan at Northwestern University write: We are seeking a Research Assistant skilled in R programming and the production of R packages. The successful applicant will have experience creating R packages accessible on github or CRAN, and ideally will have experience working with Rstan. The main […]

Eid ma clack shaw zupoven del ba.

When I say “I love you”, you look accordingly skeptical – Frida Hyvönen A few years back, Bill Callahan wrote a song about the night he dreamt the perfect song. In a fever, he woke and wrote it down before going back to sleep. The next morning, as he struggled to read his handwriting, he saw […]

Andrew vs. the Multi-Armed Bandit

Andrew and I were talking about coding up some sequential designs for A/B testing in Stan the other week. I volunteered to do the legwork and implement some examples. The literature is very accessible these days—it can be found under the subject heading “multi-armed bandits.” There’s even a Wikipedia page on multi-armed bandits that lays […]

When to add a feature to Stan? The recurring issue of the compound declare-distribute statement

At today’s Stan meeting (this is Bob, so I really do mean today), we revisited the topic of whether to add a feature to Stan that would let you put distributions on parameters with their declarations. Compound declare-define statements Mitzi added declare-define statements a while back, so you can now write: transformed parameter { real […]

New Stan case studies: NNGP and Lotka-Volterra

It’s only January and we already have two new case studies up on the Stan site. Two new case studies Lu Zhang of UCLA contributed a case study on nearest neighbor Gaussian processes. Bob Carpenter (that’s me!) of Columbia Uni contributed one on Lotka-Volterra population dynamics. Mitzi Morris of Columbia Uni has been updating her […]

State-space modeling for poll aggregation . . . in Stan!

Peter Ellis writes: As part of familiarising myself with the Stan probabilistic programming language, I replicate Simon Jackman’s state space modelling with house effects of the 2007 Australian federal election. . . . It’s not quite the model that I’d use—indeed, Ellis writes, “I’m fairly new to Stan and I’m pretty sure my Stan programs […]

Big Data Needs Big Model

Big Data are messy data, available data not random samples, observational data not experiments, available data not measurements of underlying constructs of interest. To make relevant inferences from big data, we need to extrapolate from sample to population, from control to treatment group, and from measurements to latent variables. All these steps require modeling. At […]

How productized Bayesian revenue estimation with Stan

Markus Ojala writes: Bayesian modeling is becoming mainstream in many application areas. Applying it needs still a lot of knowledge about distributions and modeling techniques but the recent development in probabilistic programming languages have made it much more tractable. Stan is a promising language that suits single analysis cases well. With the improvements in approximation […]

We were measuring the speed of Stan incorrectly—it’s faster than we thought in some cases due to antithetical sampling

Aki points out that in cases of antithetical sampling, our effective sample size calculations were unduly truncated above at the number of iterations. It turns out the effective sample size can be greater than the number of iterations if the draws are anticorrelated. And all we really care about for speed is effective sample size […]

StanCon 2018 Helsinki, 29-31 August 2018

Photo (c) Visit Helsinki / Jussi Hellsten StanCon 2018 Asilomar was so much fun that we are organizing StanCon 2018 Helsinki August 29-31, 2018 at Aalto University, Helsinki, Finland (location chosen using antithetic sampling). Full information is available at StanCon 2018 Helsinki website Summary of the information What: One day of tutorials and two days […]

Static sensitivity analysis: Computing robustness of Bayesian inferences to the choice of hyperparameters

Ryan Giordano wrote: Last year at StanCon we talked about how you can differentiate under the integral to automatically calculate quantitative hyperparameter robustness for Bayesian posteriors. Since then, I’ve packaged the idea up into an R library that plays nice with Stan. You can install it from this github repo. I’m sure you’ll be pretty […]

StanCon 2018 Live Stream — bad news…. not enough bandwidth

Breaking news: no live stream. We’re recording, so we’ll put the videos online after the fact. We don’t have enough bandwidth to live stream today.       StanCon 2018 starts today! We’re going to try our best to live stream the event on YouTube. We have the same video setup as last year, but may […]

Three new domain-specific (embedded) languages with a Stan backend

One is an accident. Two is a coincidence. Three is a pattern. Perhaps it’s no coincidence that there are three new interfaces that use Stan’s C++ implementation of adaptive Hamiltonian Monte Carlo (currently an updated version of the no-U-turn sampler). ScalaStan embeds a Stan-like language in Scala. It’s a Scala package largely (if not entirely […]

StanCon is next week, Jan 10-12, 2018

It looks pretty cool! Wednesday, Jan 10 Invited Talk: Predictive information criteria in hierarchical Bayesian models for clustered data. Sophia Rabe-Hesketh and Daniel Furr (U California, Berkely) 10:40-11:30am Does the New York City Police Department rely on quotas? Jonathan Auerbach (Columbia U) 11:30-11:50am Bayesian estimation of mechanical elastic constants. Ben Bales, Brent Goodlet, Tresa Pollock, […]

“Handling Multiplicity in Neuroimaging through Bayesian Lenses with Hierarchical Modeling”

Donald Williams points us to this new paper by Gang Chen, Yaqiong Xiao, Paul Taylor, Tracy Riggins, Fengji Geng, Elizabeth Redcay, and Robert Cox: In neuroimaging, the multiplicity issue may sneak into data analysis through several channels . . . One widely recognized aspect of multiplicity, multiple testing, occurs when the investigator fits a separate […]

Workflow, baby, workflow

Bob Carpenter writes: Here’s what we do and what we recommend everyone else do: 1. code the model as straightforwardly as possible 2. generate fake data 3. make sure the program properly codes the model 4. run the program on real data 5. *If* the model is too slow, optimize *one step at a time* […]

StanCon2018: one month to go, schedule finalized, over 20 talks, 6 tutorials… and flights are cheap

StanCon2018 is shaping up nicely as a unique opportunity to immerse oneself in all things Stan, meet Stan developers and fellow users. Registration is still open, but spots are filling up fast. We’re at 130 registrants and counting! The draft schedule is now up. We have 16 accepted talks and 6 invited talks. Posters are […]