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Archive of posts filed under the Bayesian Statistics category.

Neural nets vs. regression models

Eliot Johnson writes: I have a question concerning papers comparing two broad domains of modeling: neural nets and statistical models. Both terms are catch-alls, within each of which there are, quite obviously, multiple subdomains. For instance, NNs could include ML, DL, AI, and so on. While statistical models should include panel data, time series, hierarchical […]

“Did Jon Stewart elect Donald Trump?”

I wrote this post a couple weeks ago and scheduled it for October, but then I learned from a reporter that the research article under discussion was retracted, so it seemed to make sense to post this right away while it was still newsworthy. My original post is below, followed by a post script regarding […]

Scandal! Mister P appears in British tabloid.

Tim Morris points us to this news article: And here’s the kicker: Mister P. Not quite as cool as the time I was mentioned in Private Eye, but it’s still pretty satisfying. My next goal: Getting a mention in Sports Illustrated. (More on this soon.) In all seriousness, it’s so cool when methods that my […]

Stan examples in Harezlak, Ruppert and Wand (2018) Semiparametric Regression with R

I saw earlier drafts of this when it was in preparation and they were great. Jarek Harezlak, David Ruppert and Matt P. Wand. 2018. Semiparametric Regression with R. UseR! Series. Springer. I particularly like the careful evaluation of variational approaches. I also very much like that it’s packed with visualizations and largely based on worked […]

A thought on Bayesian workflow: calculating a likelihood ratio for data compared to peak likelihood.

Daniel Lakeland writes: Ok, so it’s really deep into the comments and I’m guessing there’s a chance you will miss it so I wanted to point at my comments here and here. In particular, the second one, which suggests something that it might be useful to recommend for Bayesian workflows: calculating a likelihood ratio for […]

Several post-doc positions in probabilistic programming etc. in Finland

There are several open post-doc positions in Aalto and University of Helsinki in 1. probabilistic programming, 2. simulator-based inference, 3. data-efficient deep learning, 4. privacy preserving and secure methods, 5. interactive AI. All these research programs are connected and collaborating. I (Aki) am the coordinator for the project 1 and contributor in the others. Overall […]

R-squared for multilevel models

Brandon Sherman writes: I just was just having a discussion with someone about multilevel models, and the following topic came up. Imagine we’re building a multilevel model to predict SAT scores using many students. First we fit a model on students only, then students in classrooms, then students in classrooms within district, the previous case […]

“Sometimes all we have left are pictures and fear”: Dan Simpson talk in Columbia stat dept, 4pm Monday

4:10pm Monday, April 22 in Social Work Bldg room 903: Data is getting weirder. Statistical models and techniques are more complex than they have ever been. No one understand what code does. But at the same time, statistical tools are being used by a wider range of people than at any time in the past. […]

The network of models and Bayesian workflow, related to generative grammar for statistical models

Ben Holmes writes: I’m a machine learning guy working in fraud prevention, and a member of some biostatistics and clinical statistics research groups at Wright State University in Dayton, Ohio. I just heard your talk “Theoretical Statistics is the Theory of Applied Statistics” on YouTube, and was extremely interested in the idea of a model-space […]

State-space models in Stan

Michael Ziedalski writes: For the past few months I have been delving into Bayesian statistics and have (without hyperbole) finally found statistics intuitive and exciting. Recently I have gone into Bayesian time series methods; however, I have found no libraries to use that can implement those models. Happily, I found Stan because it seemed among […]

All statistical conclusions require assumptions.

Mark Palko points us to this 2009 article by Itzhak Gilboa, Andrew Postlewaite, and David Schmeidler, which begins: This note argues that, under some circumstances, it is more rational not to behave in accordance with a Bayesian prior than to do so. The starting point is that in the absence of information, choosing a prior […]

Active learning and decision making with varying treatment effects!

In a new paper, Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, and Samuel Kaski write: Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action a to take for a target […]

Some Stan and Bayes short courses!

Robert Grant writes: I have a couple of events coming up that people might be interested in. They are all at Stan Taster Webinar is on 15 May, runs for one hour and is only £15. I’ll demo Stan through R (and maybe PyStan and CmdStan if the interest is there on the day), […]

What’s a good default prior for regression coefficients? A default Edlin factor of 1/2?

The punch line “Your readers are my target audience. I really want to convince them that it makes sense to divide regression coefficients by 2 and their standard errors by sqrt(2). Of course, additional prior information should be used whenever available.” The background It started with an email from Erik van Zwet, who wrote: In […]

Here’s an idea for not getting tripped up with default priors . . .

I put this in the Prior Choice Recommendations wiki awhile ago: “The prior can often only be understood in the context of the likelihood”: Here’s an idea for not getting tripped up with default priors: For each parameter (or other qoi), compare the posterior sd to the prior sd. If the posterior sd for […]

Ben Lambert. 2018. A Student’s Guide to Bayesian Statistics.

Ben Goodrich, in a Stan forums survey of Stan video lectures, points us to the following book, which introduces Bayes, HMC, and Stan: Ben Lambert. 2018. A Student’s Guide to Bayesian Statistics. SAGE Publications. If Ben Goodrich is recommending it, it’s bound to be good. Amazon reviewers seem to really like it, too. You may […]

Mister P for surveys in epidemiology — using Stan!

Jon Zelner points us to this new article in the American Journal of Epidemiology, “Multilevel Regression and Poststratification: A Modelling Approach to Estimating Population Quantities From Highly Selected Survey Samples,” by Marnie Downes, Lyle Gurrin, Dallas English, Jane Pirkis, Dianne Currier, Matthew Spittal, and John Carlin, which begins: Large-scale population health studies face increasing difficulties […]

My two talks in Montreal this Friday, 22 Mar

McGill University Biostatistics seminar, Purvis Hall, 102 Pine Ave. West, Room 25 Education Building, 3700 McTavish Street, Room 129 [note new location], 1-2pm Fri 22 Mar: Resolving the Replication Crisis Using Multilevel Modeling In recent years we have come to learn that many prominent studies in social science and medicine, conducted at leading research institutions, […]

He asks me a question, and I reply with a bunch of links

Ed Bein writes: I’m hoping you can clarify a Bayesian “metaphysics” question for me. Let me note I have limited experience with Bayesian statistics. In frequentist statistics, probability has to do with what happens in the long run. For example, a p value is defined in terms of what happens if, from now till eternity, […]

Maybe it’s time to let the old ways die; or We broke R-hat so now we have to fix it.

“Otto eye-balled the diva lying comatose amongst the reeds, and he suddenly felt the fire of inspiration flood his soul. He ran back to his workshop where he futzed and futzed and futzed.” –Bette Midler Andrew was annoyed. Well, annoyed is probably too strong a word. Maybe a better way to start is with The […]