Erling Rognli writes: I just wanted to bring your attention to a positive stats story, in case you’d want to feature it on the blog. A major journal in my field (the Journal of Child Psychology and Psychiatry) has over time taken a strong stance for using Bayesian methods, publishing an editorial in 2016 advocating switching to Bayesian methods: Editorial: Bayesian benefits for child psychology and psychiatry researchers – Oldehinkel – 2016 – Journal of Child Psychology and Psychiatry. And recently following up with inviting myself and some colleagues to write a brief introduction to Bayesian ..read more

There is a new paper in arXiv: “Supporting Bayesian modelling workflows with iterative filtering for multiverse analysis” by Anna Elisabeth Riha, Nikolas Siccha, Antti Oulasvirta, and Aki Vehtari. Anna writes An essential component of Bayesian workflows is the iteration within and across models with the goal of validating and improving the models. Workflows make the required and optional steps in model development explicit, but also require the modeller to entertain different candidate models and keep track of the dynamic set of considered models. By acknowledging the existence of multiple ca ..read more

I’ll be speaking twice at Carnegie Mellon soon. CMU statistics seminar, Fri 5 Apr 2024, 2:15pm, in Doherty Hall A302: Bayesian Workflow: Some Progress and Open Questions The workflow of applied Bayesian statistics includes not just inference but also model building, model checking, confidence-building using fake data, troubleshooting problems with computation, model understanding, and model comparison. We would like to toward codify these steps in the realistic scenario in which researchers are fitting many models for a given problem. We discuss various issues including prior distributions, d ..read more

This one from 2017 is good; I want to share it with all of you again: Our recent discussion with mathematician Russ Lyons on confidence intervals reminded me of a famous logic paradox, in which equality is not as simple as it seems. The classic example goes as follows: Abraham Lincoln is the 16th president of the United States, but this does not mean that one can substitute the two expressions “Abraham Lincoln” and “the 16th president of the United States” at will. For example, consider the statement, “If things had gone a bit differently in 1860, Stephen Douglas could have become the 16th pr ..read more

Tom Vladeck writes: I thought you may be interested in some internal research my company did using a conjoint experiment, with analysis using Stan! The upshot is that we found that vaccine hesitant people would require a large payment to take the vaccine, and that there was a substantial difference between the prices required for J&J and Moderna & Pfizer (evidence that the pause was very damaging). You can see the model code here. My reply: Cool! I recommend you remove the blank lines from your Stan code as that will make your program easier to read. Vladeck responded: I prefer a lo ..read more

Antonietta Mira is organizing a satellite workshop before ISBA. It’s free, there is still time to submit a poster, and it’s a great excuse to visit Lugano. Here are the details: Satellite workshop to International Society for Bayesian Analysis (ISBA) world meeting I really like small meetings like this. Mitzi and I are going to be there and then continue on to ISBA ..read more

Andrew recently blogged the following: Tutorial on varying-intercept, varying-slope multilevel models in Stan, from Will Hipson. This is the kind of model Andrew et al. used for one example in Red State, Blue State, which is the varying effect of income on Republican preference by state. Each state had its own slope and intercept related with a multivariate hierarchical prior. The version in Gelman and Hill’s regression book is a hack that tried to scale an inverse Wishart; the LKJ is what they would have used if Ben Goodrich had created it at that point. Andrew points to a tutorial on Bayesia ..read more

Shravan points us to these materials: Hierarchical models are bread and butter stuff for psycholinguists, so we are trying hard to make Stan/brms mainstream through various means. Teaching this stuff feels like the most important work I am doing right now, more important even than the scientific side of things. We have chapters on hierarchical modeling in our book (to be published soon with CRC Press), we use both brms and Stan: https://vasishth.github.io/bayescogsci/book/ [edit: made it a live link] The online version will remain available for free. Comments/corrections are welcome; one can ..read more

I was teaching varying-intercept, varying-slope multilevel models, and . . . I can get them to fit in Stan, but the code is kinda ugly, so I was struggling to clean it up, with no success. This will be a real research project, to add appropriate functions and possibly expand the Stan language so that these models can be written at a higher, more intuitive level. Varying-intercept models aren’t so bad. In lme4 or blme or rstanarm or brms, you write something like: y ~ 1 | group + x + z + x:z and that transfers pretty directly into Stan. Just create the X matrix and go from there. Indeed, you c ..read more

In September 2023 I taught a week-long course on statistical workflow at the Nelson Mandela African Institution of Science and Technology (NM-AIST), a public postgraduate research university in Arusha, Tanzania established in 2009. The course was hosted by Dean Professor Ernest Rashid Mbega and the Africa Centre for Research, Agricultural Advancement, Teaching Excellence and Sustainability (CREATES) through the Leader Professor Hulda Swai and Manager Rose Mosha. Our case study was an experiment on the NM-AIST campus designed and implemented by Dr Arjun Potter and Charles Luchagula to study th ..read more