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Archive of posts filed under the Bayesian Statistics category.

My talk at Yale this Thursday

It’s the Quantitative Research Methods Workshop, 12:00-1:15 p.m. in Room A002 at ISPS, 77 Prospect Street Slamming the sham: A Bayesian model for adaptive adjustment with noisy control data Andrew Gelman, Department of Statistics and Department of Political Science, Columbia University It is not always clear how to adjust for control data in causal inference, […]

How to teach sensible elementary statistics to lower-division undergraduates?

Kevin Carlson writes: Though my graduate education is in mathematics, I teach elementary statistics to lower-division undergraduates. The traditional elementary statistics curriculum culminates in confidence intervals and hypothesis tests. Most students can learn to perform these tests, but few understand them. It seems to me that there’s a great opportunity to reform the elementary curriculum […]

Hey, Stan power users! PlayStation is Hiring.

Imad writes: The Customer Lifecycle Management team at PlayStation is looking to hire a Senior Data Modeler (i.e. Data Scientist). DM me if you like building behavioral models and working with terabytes of data. You’ll have the opportunity use whatever tools you want (e.g. Stan) to build your models. I’m not into videogames myself, but […]

Non-randomly missing data is hard, or why weights won’t solve your survey problems and you need to think generatively

Throw this onto the big pile of stats problems that are a lot more subtle than they seem at first glance. This all started when Lauren pointed me at the post Another way to see why mixed models in survey data are hard on Thomas Lumley’s blog. Part of the problem is all the jargon […]

It’s Job City today for Bayesians: University of Michigan wants to hire you too!

Yajuan writes: The Survey Research Center (SRC) in the Institute for Social Research (ISR) at the University of Michigan has conducted investigator-initiated, survey-based research on theoretical and applied problems of both social and scientific importance for over 70 years (https://www.src.isr.umich.edu). SRC has over 250 research and support staff and research volumes of about $85 million […]

Givewell is hiring; wants someone to help figure out how to give well; Bayesian methods may be relevant here

Josh Rosenberg writes: GiveWell (www.givewell.org) is a nonprofit that does in-depth research to direct funds to outstanding organizations helping the global poor. In 2018, we directed more than $140 million to our recommendations. We are recruiting researchers at varying levels of seniority to identify the giving opportunities which can most cost-effectively improve the lives of […]

We’re hiring an econ postdoc!

It’s for hierarchical modeling for policy analysis in Stan. We’re really excited about this project. Will share more details soon, but wanted to get this out right away.

Bayesian analysis of data collected sequentially: it’s easy, just include as predictors in the model any variables that go into the stopping rule.

Mark Palko writes: I remember you did something on the practice of continuing to add to the sample until significance was reached. I wanted to share it with some co-workers but I can’t seem to find it on your blog. Do you remember the one I am talking about? My reply: It’s here. There’s more […]

He’s looking for a Bayesian book

Michael Lewis wrote: I’m teaching a course on Bayesian statistics this fall. I’d love to use your book but think it might be too difficult for the, mainly, graduate social work, sociology, and psychology students likely to enroll. What do you think? In response, I pointed to these two books that are more accessible than […]

The virtue of fake universes: A purposeful and safe way to explain empirical inference.

I keep being drawn to thinking there is a away to explain statistical reasoning to others that will actually do more good than harm. Now, I also keep thinking I should know better – but can’t stop.  My recent attempt starts with a shadow metaphor, then a review of analytical chemistry and moves to the […]

A heart full of hatred: 8 schools edition

No; I was all horns and thorns Sprung out fully formed, knock-kneed and upright — Joanna Newsom Far be it for me to be accused of liking things. Let me, instead, present a corner of my hateful heart. (That is to say that I’m supposed to be doing a really complicated thing right now and […]

Dan’s Paper Corner: Yes! It does work!

Only share my research With sick lab rats like me Trapped behind the beakers And the Erlenmeyer flasks Cut off from the world, I may not ever get free But I may One day Trying to find An antidote for strychnine — The Mountain Goats Hi everyone! Hope you’re enjoying Peak Libra Season! I’m bringing […]

Glenn Shafer: “The Language of Betting as a Strategy for Statistical and Scientific Communication”

Glenn Shafer writes: I have joined the immense crowd writing about p-values. My proposal is to replace them with betting outcomes: the factor by which a bet against the hypothesis multiplies the money it risks. This addresses the desideratum you and Carlin identify: embrace all the uncertainty. No one will forget that the outcome of […]

BizStat: Modeling performance indicators for deals

Ben Hanowell writes: I’ve worked for tech companies for four years now. Most have a key performance indicator that seeks to measure the rate at which an event occurs. In the simplest case, think of the event as a one-off deal, say an attempt by a buy-side real estate agent to close a deal on […]

Golf example now a Stan case study!

It’s here! (and here’s the page with all the Stan case studies). In this case study, I’m following up on two earlier posts, here and here, which in turn follow up this 2002 paper with Deb Nolan. My Stan case study is an adaptation of a model fit by Columbia business school professor and golf […]

Chow and Greenland: “Unconditional Interpretations of Statistics”

Zad Chow writes: I think your readers might find this paper [“To Aid Statistical Inference, Emphasize Unconditional Descriptions of Statistics,” by Greenland and Chow] interesting. It’s a relatively short paper that focuses on how conventional statistical modeling is based on assumptions that are often in the background and dubious, such as the presence of some […]

Laplace Calling

Laplace calling to the faraway towns Now war is declared and battle come down Laplace calling to the underworld Come out of the sample, you boys and girls Laplace calling, now don’t look to us Phony Bayesmania has bitten the dust Laplace calling, see we ain’t got no swing Except for the ring of that […]

I hate Bayes factors (when they’re used for null hypothesis significance testing)

Oliver Schultheiss writes: I am a regular reader of your blog. I am also one of those psychology researchers who were trained in the NHST tradition and who is now struggling hard to retrain himself to properly understand and use the Bayes approach (I am working on my first paper based on JASP and its […]

Here’s a puzzle: Why did the U.S. doctor tell me to drink more wine and the French doctor tell me to drink less?

This recent post [link fixed], on the health effects of drinking a glass of wine a day, reminds me of a story: Several years ago my cardiologist in the U.S. recommended that I drink a glass of red wine a day for health reasons. I’m not a big drinker—probably I average something less than 100 […]

Calibration and sharpness?

I really liked this paper, and am curious what other people think before I base a grant application around applying Stan to this problem in a machine-learning context. Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007). Probabilistic forecasts, calibration and sharpness. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(2), 243–268. Gneiting […]