Skip to content
Archive of posts filed under the Bayesian Statistics category.

Ma conférence demain (mardi) à l’École Polytechnique

À 11h15 au Centre de Mathématiques Appliquées: Peut-on utiliser les méthodes bayésiennes pour résoudre la crise des résultats de la recherche statistiquement significatifs que ne tiennent pas? It’s the usual story: the audience will be technical but with a varying mix of interests, and so what they most wanted to hear was something general and […]

Problematic interpretations of confidence intervals

Rink Hoekstra writes: A couple of months ago, you were visiting the University of Groningen, and after the talk you gave there I spoke briefly with you about a study that I conducted with Richard Morey, Jeff Rouder and Eric-Jan Wagenmakers. In the study, we found that researchers’  knowledge of how to interpret a confidence interval […]

Stan Model of the Week: PK Calculation of IV and Oral Dosing

[Update: Revised given comments from Wingfeet, Andrew and germo. Thanks! I'd mistakenly translated the dlnorm priors in the first version --- amazing what a difference the priors make. I also escaped the less-than and greater-than signs in the constraints in the model so they're visible. I also updated to match the thin=2 output of JAGS.] […]

Running into a Stan Reference by Accident

We were talking about parallelizing MCMC and I came up with what I thought was a neat idea for parallelizing MCMC (sample with fractional prior, average samples on a per-draw basis). But then I realized this approach could get the right posterior mean or right posterior variance, but not both, depending on how the prior […]

Basketball Stats: Don’t model the probability of win, model the expected score differential.

Someone who wants to remain anonymous writes: I am working to create a more accurate in-game win probability model for basketball games. My idea is for each timestep in a game (a second, 5 seconds, etc), use the Vegas line, the current score differential, who has the ball, and the number of possessions played already […]

Postdoc with Huffpost Pollster to do Bayesian poll tracking

Mark Blumenthal writes: HuffPost Pollster has an immediate opening for a social and data scientist to join us full time, preferably in our Washington D.C. bureau, to work on development and improvement of our poll tracking models and political forecasts. You are someone who has: * A passion for electoral politics, * Advanced training in […]

Stopping rules and Bayesian analysis

I happened to receive two questions about stopping rules on the same day. First, from Tom Cunningham: I’ve been arguing with my colleagues about whether the stopping rule is relevant (a presenter disclosed that he went out to collect more data because the first experiment didn’t get significant results) — and I believe you have […]

How to think about “identifiability” in Bayesian inference?

We had some questions on the Stan list regarding identification. The topic arose because people were fitting models with improper posterior distributions, the kind of model where there’s a ridge in the likelihood and the parameters are not otherwise constrained. I tried to help by writing something on Bayesian identifiability for the Stan list. Then […]

Prior distribution for a predicted probability

I received the following email: I have an interesting thought on a prior for a logistic regression, and would love your input on how to make it “work.” Some of my research, two published papers, are on mathematical models of **. Along those lines, I’m interested in developing more models for **. . . . […]

Xihong Lin on sparsity and density

I pointed Xihong Lin to this post from last month regarding Hastie and Tibshirani’s “bet on sparsity principle.” I argued that, in the worlds in which I work, in social and environmental science, every contrast is meaningful, even if not all of them can be distinguished from noise given a particular dataset. That is, I […]