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Archive of posts filed under the Stan category.

Gary Venter’s age-period-cohort decomposition of US male mortality trends

Following up on yesterday’s post on mortality trends, I wanted to share with you a research note by actuary Gary Venter, “A Quick Look at Cohort Effects in US Male Mortality.” Venter produces this graph: And he writes: Cohort effects in mortality tend to be difficult to explain. Often strings of coincidences are invoked – […]

Lots of buzz regarding this postdoc position in London

Tom Churcher writes: We are currently advertising for an infectious disease modeller to investigate the impact of insecticide resistance on malaria control in Africa. The position is for 3 years in the first instance and is funded by the Wellcome Trust. No previous malaria or mosi experience required. Please circulate to anyone who might be […]

Stan Coding Corner: O(N) Change-Point Program with Clever Forward-Backward Calculation

It’s so much fun to work in open source. Luke Wiklendt sent along this improved code for a change-point model calculation in Stan. With N data points in the time series, the version in the manual is O(N2), whereas the improved version is O(N). In practice, Luke says [the new code] results in a dramatic […]

Data-dependent prior as an approximation to hierarchical model

Andy Solow writes: I have a question about Bayesian statistics. Why is it wrong to use the same data to formulate the prior and to update it to the posterior? I am having a hard time coming up with – or finding in the literature – a formal reason. I asked him to elaborate and […]

I definitely wouldn’t frame it as “To determine if the time series has a change-point or not.” The time series, whatever it is, has a change point at every time. The question might be, “Is a change point necessary to model these data?” That’s a question I could get behind.

From the Stan users list: I’m trying out to fit a time series that can have 0 or 1 change point using the sample model from Ch 11.2 of the manual. To determine if the time series has a change-point or not, would I need to do comparison model (via loo) between 1-change model developed […]

Stan Case Studies Launches

There’s a new section of the Stan web site, with case studies meant to illustrate statistical methodologies, classes of models, application areas, statistical computation, and Stan programming. Stan Case Studies The first ten or so are up, including a grab bag of education models from Daniel Furr at U.C. Berkeley: Hierarchical Two-Parameter Logistic Item Response […]

Stan backronym just a joke

Stan is named after Stanislaw Ulam, the inventor of the Monte Carlo method. At one point we were joking around and I came up with the backronym Sampling Through Adaptive Neighborhoods. I kinda like this as a backronym but it was really just a joke. Stan does not stand for Sampling Through Adaptive Neighborhoods. Stan […]

Bayesian inference for network links

A colleague writes: I’m working with a doctoral student on a latent affinity network problem and we keep hitting challenges in sampling, in our case using Metropolis-Hastings, for the network links. As you can imagine, lots of local modes, things get stuck, etc . . . Any suggestions on how to sample network links? My […]

Stan – The Bayesian Data Scientist’s Best Friend

My friend Juuso Parkkinen has interesting Stan related blog, which is worth following. The above cool animation is from today’s post discussing the updated results of using Stan to model apartment prices in Finland. Few weeks ago Juuso also blogged about a probabilistic programming seminar in Finland with a title Stan – The Bayesian Data […]

Fitting the birthday model in Stan

I’m scheduling these posts a few months ahead of time, and I realize this is the perfect date for an update on the birthday model. Can we fit in Stan yet? As of this writing, I don’t know. But Aki and Seth assure me that we’re close . . . P.S. Happy 13th birthday, Craig!

Nonprofit Stan!

Stan is now linked to NumFocus, a 501(c)(3) nonprofit organization that serves several other open-source software projects, including NumPy, Julia, Jupyter, and others: Stan can now accept tax-deductible contributions through this foundation. If you’re interested in donating to Stan, you can contact us directly or just go through the donation page. Stan is a worthy […]

Stan does Valentine’s

Today’s story starts with a bit of statistically-related fluff, a news report from Emily Crockett entitled, “Here’s how much people in your state spend on Valentine’s Day gifts”: A survey by Finder.com asked 3,121 Americans how much they spend on Valentine’s Day gifts for their loved ones, and figured out which US states spend the […]

“Don’t get me started on ‘cut'”

Brendan Rocks writes: I have a request for a blog post. I’ve been following the debates about ‘cut’ on the Stan lists over the last few years. Lots of very clever people agree that it’s bad news, which is enough to put me off. However, I’ve never fully groked the reasoning. [I think that should […]

Black Hole Stan

We told Will Farr, a professor at the University of Birmingham who is part of the recent headline-grabbing experiment that corroborated Einstein’s theory of general relativity, that we blurbed Stan’s involvement in that project, and Farr wrote: We used PyStan pretty extensively in the rates group—we have some simple analytic posteriors for the rates stuff, […]

The recent black hole LIGO experiment used PyStan!

Bob links to the recently celebrated physics paper. Check it out: And in the references: This is even better than being quoted in Private Eye! P.S. More here.

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 3–9 so far, 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 […]