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Archive of posts filed under the Bayesian Statistics category.

Steven Pinker on torture

I’ve recently been thinking about that expression, “A liberal is a conservative who’s been arrested.” Linguist and public intellectual Steven Pinker got into some trouble recently when it turned out that he’d been offering expert advice to the legal team of now-disgraced financier Jeffrey Epstein. I would not condemn Pinker for this. After all, everybody […]

Hey—the New York Times is hiring an election forecaster!

Chris Wiggins points us to this job opening: Staff Editor – Statistical Modeling The New York Times is looking to increase its capacity for statistical projects in the newsroom, especially around the 2020 election. You will help produce statistical forecasts for election nights, as part of The Times’s ambitious election results operation. That operation is […]

How to “cut” using Stan, if you must

Frederic Bois writes: We had talked at some point about cutting inference in Stan (that is, for example, calibrating PK parameters in a PK/PD [pharmacokinetic/pharmacodynamic] model with PK data, then calibrating the PD parameters, with fixed, non updated, distributions for the PK parameters). Has that been implemented? (PK is pharmacokinetic and PD is pharmacodynamic.) I […]

Smoothness, or lack thereof, in MRP estimates over time

Matthew Loop writes: I’m taking my first crack at MRP. We are estimating the probability of an event over 30 years, adjusting for sampling stratum using a multilevel model with varying intercepts for stratum. When we fit the model, the marginal predicted probability vs. year is a smooth function, since the mean of the varying […]

Criminologists be (allegedly) crimin’ . . . and a statistical New Year’s toast for you.

Someone who wishes to remain anonymous points us to this video, writing: It has to do with Stewart at FSU, in criminology. Couldn’t produce a survey that was the basis for 5 papers, all retracted. FSU though still failed to do complete investigation. The preliminary investigation had a 3 person panel, 2 of whom were […]

Do we still recommend average predictive comparisons? Click here to find the surprising answer!

Usually these posts are on 6-month delay but this one’s so quick I thought I’d just post it now . . . Daniel Habermann writes: Do you still like/recommend average predictive comparisons as described in your paper with Iain Pardoe? I [Habermann] find them particularly useful for summarizing logistic regression models. My reply: Yes, I […]

Fitting big multilevel regressions in Stan?

Joe Hoover writes: I am a social psychology PhD student, and I have some questions about applying MrP to estimation problems involving very large datasets or many sub-national units. I use MrP to obtain sub-national estimates for low-level geographic units (e.g. counties) derived from large data (e.g. 300k-1 million+). In addition to being large, my […]

They’re looking to hire a Bayesian.

Ty Beal writes: The Global Alliance for Improved Nutrition (GAIN) is looking for an analyst with expertise in Bayesian methods. Could you share this post with qualified and interested candidates. The job is based in Washington DC.

Response to criticisms of Bayesian statistics

I just happened to reread this article of mine from 2008, and I still like it! So I’m linking to it here. Enjoy.

Beautiful paper on HMMs and derivatives

I’ve been talking to Michael Betancourt and Charles Margossian about implementing analytic derivatives for HMMs in Stan to reduce memory overhead and increase speed. For now, one has to implement the forward algorithm in the Stan program and let Stan autodiff through it. I worked out the adjoint method (aka reverse-mode autodiff) derivatives of the […]

Postdoctoral research position on survey research with us at Columbia School of Social Work

Here it is: The Center on Poverty and Social Policy at the Columbia University School of Social Work, the Columbia Population Research Center, and the Institute for Social and Economic Research and Policy are seeking a postdoctoral scholar with a PhD in statistics, economics, political science, public policy, demography, psychology, social work, sociology, or a […]

Field goal kicking—like putting in 3D with oblong balls

Putting Andrew Gelman (the author of most posts on this blog, but not this one), recently published a Stan case study on golf putting [link fixed] that uses a bit of geometry to build a regression-type model based on angles and force. Field-goal kicking In American football, there’s also a play called a “field goal.” […]

What’s wrong with Bayes

My problem is not just with the methods—although I do have problems with the method—but also with the ideology. My problem with the method It’s the usual story. Bayesian inference is model-based. Your model will never be perfect, and if you push hard you can find the weak points and magnify them until you get […]

A Bayesian view of data augmentation.

This post is by Keith O’Rourke and as with all posts and comments on this blog, is just a deliberation on dealing with uncertainties in scientific inquiry and should not to be attributed to any entity other than the author. As with any critically-thinking inquirer, the views behind these deliberations are always subject to rethinking […]

What’s wrong with Bayes; What’s wrong with null hypothesis significance testing

This will be two posts: tomorrow: What’s wrong with Bayes day after tomorrow: What’s wrong with null hypothesis significance testing My problem in each case is not just with the methods—although I do have problems with the methods—but also with the ideology. A future post or article: Ideologies of Science: Their Advantages and Disadvantages.

In short, adding more animals to your experiment is fine. The problem is in using statistical significance to make decisions about what to conclude from your data.

Denis Jabaudon writes: I was thinking that perhaps you could help me with the following “paradox?” that I often find myself in when discussing with students (I am a basic neuroscientist and my unit of counting is usually cells or animals): When performing a “pilot” study on say 5 animals, and finding an “almost significant” […]

The default prior for logistic regression coefficients in Scikit-learn

Someone pointed me to this post by W. D., reporting that, in Python’s popular Scikit-learn package, the default prior for logistic regression coefficients is normal(0,1)—or, as W. D. puts it, L2 penalization with a lambda of 1. In the post, W. D. makes three arguments. I agree with two of them. 1. I agree with […]

“Machine Learning Under a Modern Optimization Lens” Under a Bayesian Lens

I (Yuling) read this new book Machine Learning Under a Modern Optimization Lens (by Dimitris Bertsimas and Jack Dunn) after I grabbed it from Andrew’s desk. Apparently machine learning is now such a wide-ranging area that we have to access it through some sub-manifold so as to evade dimension curse, and it is the same […]

No, Bayes does not like Mayor Pete. (Pitfalls of using implied betting market odds to estimate electability.)

Asher Meir points to this amusing post from Greg Mankiw, who writes: Who has the best chance of beating Donald Trump? A clue can be found using Bayes Theorem. Here is the logic. Let A be the event that a candidate wins the general election, and B be the event that a candidate wins his […]

Econometrics postdoc and computational statistics postdoc openings here in the Stan group at Columbia

Andrew and I are looking to hire two postdocs to join the Stan group at Columbia starting January 2020. I want to emphasize that these are postdoc positions, not programmer positions. So while each position has a practical focus, our broader goal is to carry out high-impact, practical research that pushes the frontier of what’s […]