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Archive of posts filed under the Bayesian Statistics category.

How good is the Bayes posterior for prediction really?

It might not be common courtesy of this blog to make comments on a very-recently-arxiv-ed paper. But I have seen two copies of this paper entitled “how good is the Bayes posterior in deep neural networks really” left on the tray of the department printer during the past weekend, so I cannot underestimate the popularity of […]

MRP Conference at Columbia April 3rd – April 4th 2020

The Departments of Statistics and Political Science and Institute for Social and Economic Research and Policy at Columbia University are delighted to invite you to our Spring conference on Multilevel Regression and Poststratification. Featuring Andrew Gelman, Beth Tipton, Jon Zelner, Shira Mitchell, Qixuan Chen and Leontine Alkema, the conference will combine a mix of cutting […]

Abuse of expectation notation

I’ve been reading a lot of statistical and computational literature and it seems like expectation notation is absued as shorthand for integrals by decorating the expectation symbol with a subscripted distribution like so: This is super confusing, because expectations are properly defined as functions of random variables. For example, the square bracket convention arises because […]

Fugitive and cloistered virtues

There’s a new revolution, a loud evolution that I saw. Born of confusion and quiet collusion of which mostly I’ve known. — Lana Del Ray While an unexamined life seems like an excellent idea, an unexamined prior distribution probably isn’t. And, I mean, I write and talk and think and talk and write and talk and talk and […]

Forget about multiple testing corrections. Actually, forget about hypothesis testing entirely.

Tai Huang writes: I am reading this paper [Why we (usually) don’t have to worry about multiple comparisons, by Jennifer, Masanao, and myself]. I am searching how to do multiple comparisons correctly under Bayesian inference for A/B/C testing. For the traditional t-test approach, Bonferroni correction is needed to correct alpha value. I am confused with […]

Rao-Blackwellization and discrete parameters in Stan

I’m reading a really dense and beautifully written survey of Monte Carlo gradient estimation for machine learning by Shakir Mohamed, Mihaela Rosca, Michael Figurnov, and Andriy Mnih. There are great explanations of everything including variance reduction techniques like coupling, control variates, and Rao-Blackwellization. The latter’s the topic of today’s post, as it relates directly to […]

Causal inference in AI: Expressing potential outcomes in a graphical-modeling framework that can be fit using Stan

David Rohde writes: We have been working on an idea that attempts to combine ideas from Bayesian approaches to causality developed by you and your collaborators with Pearl’s do calculus. The core idea is simple, but we think powerful and allows some problems previously that only had known solutions with the do calculus to be […]

In Bayesian inference, do people cheat by rigging the prior?

Ulrich Atz writes in with a question: A newcomer to Bayesian inference may argue that priors seem sooo subjective and can lead to any answer. There are many counter-arguments (e.g., it’s easier to cheat in other ways), but are there any pithy examples where scientists have abused the prior to get to the result they […]

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.” […]