After three years, we finally have an updated version of our “EP as a way of life” paper. Authors are Andrew Gelman, Aki Vehtari, Pasi Jylänki, Tuomas Sivula, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John Cunningham, David Schiminovich, and Christian Robert. Aki deserves credit for putting this all together into a coherent whole. Here’s the […] The post Expectation propagation as a way of life: A framework for Bayesian inference…
Following on from his talk at StanCon, Michael Betancourt just wrote three Stan case studies, all of which are must reads: Diagnosing Biased Inference with Divergences: This case study discusses the subtleties of accurate Markov chain Monte Carlo estimation and how divergences can be used to identify biased estimation in practice. Identifying Bayesian Mixture […] The post A fistful of Stan case studies: divergences and bias, identifying mixtures, and…
Add one feature to your model and test and debug with fake data before going on. Don’t try to add two features at once. The post Advice when debugging at 11pm appeared first on Statistical Modeling, Causal Inference, and Social Science.
Sean Taylor, a research scientist at Facebook and Stan user, writes: I wanted to tell you about an open source forecasting package we just released called Prophet: I thought the readers of your blog might be interested in both the package and the fact that we built it on top of Stan. Under the hood, […] The post Facebook’s Prophet uses Stan appeared first on Statistical Modeling, Causal Inference, and…
Now when I look at my old R code, it looks really weird because there are no semicolons Each line of code just looks incomplete As if I were writing my sentences like this Whassup with that, huh Also can I please no longer do <- I much prefer = Please The post Exposure to Stan has changed my defaults: a non-haiku appeared first on Statistical Modeling, Causal Inference, and…
Someone on the users list asked about lasso regression in Stan, and Ben replied: In the rstanarm package we have stan_lm(), which is sort of like ridge regression, and stan_glm() with family = gaussian and prior = laplace() or prior = lasso(). The latter estimates the shrinkage as a hyperparameter while the former fixes it […] The post Lasso regression etc in Stan appeared first on Statistical Modeling, Causal Inference,…
I was having a conversation with Andrew that went like this yesterday: Andrew: Hey, someone’s giving a talk today on HMMs (that someone was Yang Chen, who was giving a talk based on her JASA paper Analyzing single-molecule protein transportation experiments via hierarchical hidden Markov models). Maybe we should add some specialized discrete modules to […] The post HMMs in Stan? Absolutely! appeared first on Statistical Modeling, Causal Inference, and…
You can fit finite mixture models in Stan; see section 12 of the Stan manual. You can fit change point models in Stan; see section 14.2 of the Stan manual. You can fit mark-recapture models in Stan; see section 14.2 of the Stan manual. You can fit hidden Markov models in Stan; see section 9.6 […] The post You can fit hidden Markov models in Stan (and thus, also in…
Thank you all for coming and making the first Stan Conference a success! The organizers were blown away by how many people came to the first conference. We had over 150 registrants this year! StanCon 2017 Video The organizers managed to get a video stream on YouTube: https://youtu.be/DJ0c7Bm5Djk. We have over 1900 views since StanCon! (We lost […] The post Thanks for attending StanCon 2017! appeared first on Statistical Modeling, Causal Inference,…
Stan is an open-source, state-of-the-art probabilistic programming language with a high-performance Bayesian inference engine written in C++. Stan had been successfully applied to modeling problems with hundreds of thousands of parameters in fields as diverse as econometrics, sports analytics, physics, pharmacometrics, recommender systems, political science, and many more. Research using Stan has been featured in […] The post Come and work with us! appeared first on Statistical Modeling, Causal Inference,…
