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Archive of posts filed under the Stan category.

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: […]

Workshop on Interpretable Machine Learning

Andrew Gordon Wilson sends along this conference announcement: NIPS 2017 Symposium Interpretable Machine Learning Long Beach, California, USA December 7, 2017 Call for Papers: We invite researchers to submit their recent work on interpretable machine learning from a wide range of approaches, including (1) methods that are designed to be more interpretable from the start, […]

Partial pooling with informative priors on the hierarchical variance parameters: The next frontier in multilevel modeling

Ed Vul writes: In the course of tinkering with someone else’s hairy dataset with a great many candidate explanatory variables (some of which are largely orthogonal factors, but the ones of most interest are competing “binning” schemes of the same latent elements). I wondered about the following “model selection” strategy, which you may have alluded […]

Splines in Stan; Spatial Models in Stan !

Two case studies: Splines in Stan, by Milad Kharratzadeh. Spatial Models in Stan: Intrinsic Auto-Regressive Models for Areal Data, by Mitzi Morris. This is great. Thanks, Mitzi! Thanks, Milad!

Tenure-Track or Tenured Prof. in Machine Learning in Aalto, Finland

This job advertisement for a position in Aalto, Finland, is by Aki We are looking for a professor to either further strengthen our strong research fields, with keywords including statistical machine learning, probabilistic modelling, Bayesian inference, kernel methods, computational statistics, or complementing them with deep learning. Collaboration with other fields is welcome, with local opportunities […]

Stan Roundup, 6 October 2017

I missed last week and almost forgot to add this week’s. Jonah Gabry returned from teaching a one-week course for a special EU research institute in Spain. Mitzi Morris has been knocking out bug fixes for the parser and some pull requests to refactor the underlying type inference to clear the way for tuples, sparse […]

Getting the right uncertainties when fitting multilevel models

Cesare Aloisi writes: I am writing you regarding something I recently stumbled upon in your book Data Analysis Using Regression and Multilevel/Hierarchical Models which confused me, in hopes you could help me understand it. This book has been my reference guide for many years now, and I am extremely grateful for everything I learnt from […]

Stan Weekly Roundup, 22 September 2017

This week (and a bit from last week) in Stan: Paul-Christian Bürkner‘s paper on brms (a higher-level interface to RStan, which preceded rstanarm and is still widely used and recommended by our own devs) was just published as a JStatSoft article. If you follow the link, the abstract explains what brms does. Ben Goodrich and […]