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Archive of posts filed under the Stan category.

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 […]

How not to compare the speed of Stan to something else

Someone’s wrong on the internet And I have to do something about it. Following on from Dan’s post on Barry Gibb statistical model evaluation, here’s an example inspired by a paper I found on Google Scholar searching for Stan citations. The paper (which there is no point in citing) concluded that JAGS was faster than […]

Computational and statistical issues with uniform interval priors

There are two anti-patterns* for prior specification in Stan programs that can be sourced directly to idioms developed for BUGS. One is the diffuse gamma priors that Andrew’s already written about at length. The second is interval-based priors. Which brings us to today’s post. Interval priors An interval prior is something like this in Stan […]

Stan is a probabilistic programming language

See here: Stan: A Probabilistic Programming Language. Journal of Statistical Software. (Bob Carpenter, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, Allen Riddell) And here: Stan is Turing Complete. So what? (Bob Carpenter) And, the pre-stan version: Fully Bayesian computing. (Jouni Kerman and Andrew Gelman) Apparently […]

Wine + Stan + Climate change = ?

Pablo Almaraz writes: Recently, I published a paper in the journal Climate Research in which I used RStan to conduct the statistical analyses: Almaraz P (2015) Bordeaux wine quality and climate fluctuations during the last century: changing temperatures and changing industry. Clim Res 64:187-199.

Custom Distribution Solutions

I (Aki) recently made a case study that demonstrates how to implement user defined probability functions in Stan language (case study, git repo). As an example I use the generalized Pareto distribution (GPD) to model extreme values of geomagnetic storm data from the World Data Center for Geomagnetism. Stan has had support for user defined […]

Computing marginal likelihoods in Stan, from Quentin Gronau and E. J. Wagenmakers

Gronau and Wagemakers write: The bridgesampling package facilitates the computation of the marginal likelihood for a wide range of different statistical models. For models implemented in Stan (such that the constants are retained), executing the code bridge_sampler(stanfit) automatically produces an estimate of the marginal likelihood. Full story is at the link.

Stan Roundup, 10 November 2017

We’re in the heart of the academic season and there’s a lot going on. James Ramsey reported a critical performance regression bug in Stan 2.17 (this affects the latest CmdStan and PyStan, not the latest RStan). Sean Talts and Daniel Lee diagnosed the underlying problem as being with the change from char* to std::string arguments—you […]

Using Stan to improve rice yields

Matt Espe writes: Here is a new paper citing Stan and the rstanarm package. Yield gap analysis of US rice production systems shows opportunities for improvement. Matthew B. Espe, Kenneth G. Cassman, Haishun Yang, Nicolas Guilpart, Patricio Grassini, Justin Van Wart, Merle Anders, Donn Beighley, Dustin Harrell, Steve Linscombe, Kent McKenzie, Randall Mutters, Lloyd T. […]

StanCon2018 Early Registration ends Nov 10

StanCon is happening at the beautiful Asilomar conference facility at the beach in Monterey California for three days starting January 10, 2018. We have space for 200 souls and this will sell out. If you don’t already know, Stan is the rising star of probabilistic modeling with Bayesian analysis. If you do statistics, machine learning […]

Stan Roundup, 27 October 2017

I missed two weeks and haven’t had time to create a dedicated blog for Stan yet, so we’re still here. This is only the update for this week. From now on, I’m going to try to concentrate on things that are done, not just in progress so you can get a better feel for the […]

My 2 talks in Seattle this Wed and Thurs: “The Statistical Crisis in Science” and “Bayesian Workflow”

For the Data Science Seminar, Wed 25 Oct, 3:30pm in Physics and Astronomy Auditorium – A102: The Statistical Crisis in Science Top journals routinely publish ridiculous, scientifically implausible claims, justified based on “p < 0.05.” And this in turn calls into question all sorts of more plausible, but not necessarily true, claims, that are supported […]

The network of models and Bayesian workflow

This is important, it’s been something I’ve been thinking about for decades, it just came up in an email I wrote, and it’s refreshingly unrelated to recent topics of blog discussion, so I decided to just post it right now out of sequence (next slot on the queue is in May 2018). Right now, standard […]

Analyzing New Zealand fatal traffic crashes in Stan with added open-access science

Open-access science I’ll get to the meat of this post in a second, but I just wanted to highlight how the study I’m about to talk about was done in the open and how that helped everyone. Tim Makarios read the study and responded in the blog comments, Hold on. As I first skimmed this […]

Baseball, apple pie, and Stan

Ben sends along these two baseball job ads that mention experience with Stan as a preferred qualification: St. Louis Cardinals Baseball Development Analyst Tampa Bay Rays Baseball Research and Development Analyst

Stan case studies

Following up on recent posts here and here, I thought I’d post a list of all the Stan case studies we have so far. 2017: Modeling Loss Curves in Insurance with RStan, by Mick Cooney Splines in Stan, by Milad Kharratzadeh Spatial Models in Stan: Intrinsic Auto-Regressive Models for Areal Data, by Mitzi Morris The […]

Mick Cooney: case study on modeling loss curves in insurance with RStan

This is great. Thanks, Mick! All the Stan case studies are here.

Postdoc in Finland and NY to work on probabilistic inference and Stan!

I (Aki) got 2 year funding to hire a postdoc to work on validation of probabilistic inference approaches and model selection in Stan. Work would be done with Stan team in Aalto, Helsinki and Columbia, New York. We probably have PhD positions, too. The funding is part of the joint project with Antti Honkela and […]

Halifax, NS, Stan talk and course Thu 19 Oct

Halfiax, here we come. I (Bob, not Andrew) am going to be giving a talk on Stan and then Mitzi and I will be teaching a course on Stan after that. The public is invited, though space is limited for the course. Here are details if you happen to be in the Maritime provinces. TALK: […]