Milad Kharratzadeh shares a new case study. This could be useful to a lot of people. And here’s the markdown file with every last bit of R and Stan code. Just for example, here’s the last section of the document, which shows how to simulate the data and fit the model graphed above: Location of […] The post Splines in Stan! (including priors that enforce smoothness) appeared first on Statistical…
link R doesn’t allow block comments. You have to comment out each line, or you can encapsulate the block in if(0){} which is the world’s biggest hack. Grrrrr. P.S. Just to clarify: I want block commenting not because I want to add long explanatory blocks of text to annotate my scripts. I want block commenting […] The post I hate R, volume 38942 appeared first on Statistical Modeling, Causal Inference,…
Given that Andrew started the Gremlin theme, I thought it would only be fitting to link to the following amusing blog post: Chris Brown: Choosing R packages for mixed effects modelling based on the car you drive (on the seascape models blog) It’s exactly what it says on the tin. I won’t spoil the punchline, […] The post Fitting hierarchical GLMs in package X is like driving car Y appeared…
Time to get positive. I was asking Andrew whether it’s true that I have the right coverage in Bayesian posterior intervals if I generate the parameters from the prior and the data from the parameters. He replied that yes indeed that is true, and directed me to: Cook, S.R., Gelman, A. and Rubin, D.B. 2006. […] The post Bayesian Posteriors are Calibrated by Definition appeared first on Statistical Modeling, Causal…
This post is by Aki. We have often been asked in the Stan user forum how to do model combination for Stan models. Bayesian model averaging (BMA) by computing marginal likelihoods is challenging in theory and even more challenging in practice using only the MCMC samples obtained from the full model posteriors. Some users have […] The post Stacking, pseudo-BMA, and AIC type weights for combining Bayesian predictive distributions appeared…
Scalable Bayesian Inference with Hamiltonian Monte Carlo Despite the promise of big data, inferences are often limited not by sample size but rather by systematic effects. Only by carefully modeling these effects can we take full advantage of the data—big data must be complemented with big models and the algorithms that can fit them. One […] The post “Scalable Bayesian Inference with Hamiltonian Monte Carlo” (Michael Betancourt’s talk this Thurs…
Ben writes: Starting with the 2.13 release, it is much easier to use external C++ code in a Stan program. This vignette briefly illustrates how to do so. He continues: Suppose that you have (part of) a Stan program that involves Fibonacci numbers, such as functions { int fib(int n); int fib(int n) { if […] The post Running Stan with external C++ code appeared first on Statistical Modeling, Causal…
Ensemble methods By ensemble methods, I (Bob, not Andrew) mean approaches that scatter points in parameter space and then make moves by inteprolating or extrapolating among subsets of them. Two prominent examples are: Ter Braak’s differential evolution Goodman and Weare’s walkers There are extensions and computer implementations of these algorithms. For example, the Python […] The post Ensemble Methods are Doomed to Fail in High Dimensions appeared first on…
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…
