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Archive of posts filed under the Bayesian Statistics category.

He wants to get started on Bayes

Mathew Mercuri writes: I am interested in learning how to work in a Bayesian world. I have training in a frequentist approach, specifically from an applied health scientist/epidemiologist approach. However, while i teach courses in applied statistics, I am not particularly savvy with heavy statistical mathematics, so I am a bit worried bout how to […]

Trump +1 in Florida; or, a quick comment on that “5 groups analyze the same poll” exercise

Nate Cohn at the New York Times arranged a comparative study on a recent Florida pre-election poll. He sent the raw data to four groups (Charles Franklin; Patrick Ruffini; Margie Omero, Robert Green, Adam Rosenblatt; and Sam Corbett-Davies, David Rothschild, and me) and asked each of us to analyze the data how we’d like to […]

“Crimes Against Data”: My talk at Ohio State University this Thurs; “Solving Statistics Problems Using Stan”: My talk at the University of Michigan this Fri

Crimes Against Data Statistics has been described as the science of uncertainty. But, paradoxically, statistical methods are often used to create a sense of certainty where none should exist. The social sciences have been rocked in recent years by highly publicized claims, published in top journals, that were reported as “statistically significant” but are implausible […]

Let’s play Twister, let’s play Risk

Alex Terenin, Dan Simpson, and David Draper write: Some months ago we shared with you an arxiv draft of our paper, Asynchronous Distributed Gibbs Sampling.​ Through comments we’ve received, for which we’re highly grateful, we came to understand that (a) our convergence proof was wrong, and (b) we actually have two algorithms, one exact and […]

Solving Statistics Problems Using Stan (my talk at the NYC chapter of the American Statistical Association)

Here’s the announcement: Solving Statistics Problems Using Stan Stan is a free and open-source probabilistic programming language and Bayesian inference engine. In this talk, we demonstrate the use of Stan for some small fun problems and then discuss some open problems in Stan and in Bayesian computation and Bayesian inference more generally. It’s next Tues, […]

Bayesian Statistics Then and Now

I happened to recently reread this article of mine from 2010, and I absolutely love it. I don’t think it’s been read by many people—it was published as one of three discussions of an article by Brad Efron in Statistical Science—so I wanted to share it with you again here. This is the article where […]

Hypothesis Testing is a Bad Idea (my talk at Warwick, England, 2pm Thurs 15 Sept)

This is the conference, and here’s my talk (will do Google hangout, just as with my recent talks in Bern, Strasbourg, etc): Hypothesis Testing is a Bad Idea Through a series of examples, we consider problems with classical hypothesis testing, whether performed using classical p-values or confidence intervals, Bayes factors, or Bayesian inference using noninformative […]

Q: “Is A 50-State Poll As Good As 50 State Polls?” A: Use Mister P.

Jeff Lax points to this post from Nate Silver and asks for my thoughts. In his post, Nate talks about data quality issues of national and state polls. It’s a good discussion, but the one thing he unfortunately doesn’t talk about is multilevel regression and poststratification (or see here for more). What you want to […]

Stan users group hits 2000 registrations

Of course, there are bound to be duplicate emails, dead emails, and people who picked up Stan, joined the list, and never came back. But still, that’s a lot of people who’ve expressed interest! It’s been an amazing ride that’s only going to get better as we learn more and continue to improve Stan’s speed […]

Exploration vs. exploitation tradeoff

Alon Levy (link from Palko) looks into “Hyperloop, a loopy intercity rail transit idea proposed by Tesla Motors’ Elon Musk, an entrepreneur who hopes to make a living some day building cars,” and writes: There is a belief within American media that a successful person can succeed at anything. He (and it’s invariably he) is […]

In Bayesian regression, it’s easy to account for measurement error

Mikhail Balyasin writes: I have come across this paper by Jacob Westfall and Tal Yarkoni, “Statistically Controlling for Confounding Constructs Is Harder than You Think.” I think it talks about very similar issues you raise on your blog, but in this case they advise to use SEM [structural equation models] to control for confounding constructs. […]

Fast CAR: Two weird tricks for fast conditional autoregressive models in Stan

Max Joseph writes: Conditional autoregressive (CAR) models are popular as prior distributions for spatial random effects with areal spatial data. Historically, MCMC algorithms for CAR models have benefitted from efficient Gibbs sampling via full conditional distributions for the spatial random effects. But, these conditional specifications do not work in Stan, where the joint density needs […]

Free workshop on Stan for pharmacometrics (Paris, 22 September 2016); preceded by (non-free) three day course on Stan for pharmacometrics

So much for one post a day… Workshop: Stan for Pharmacometrics Day If you are interested in a free day of Stan for pharmacometrics in Paris on 22 September 2016, see the registration page: Stan for Pharmacometrics Day (free workshop) Julie Bertrand (statistical pharmacologist from Paris-Diderot and UCL) has finalized the program: When Who What […]

Hey pollsters! Poststratify on party ID, or we’re all gonna have to do it for you.

Alan Abramowitz writes: In five days, Clinton’s lead increased from 5 points to 12 points. And Democratic party ID margin increased from 3 points to 10 points. No, I don’t think millions of voters switched to the Democratic party. I think Democrats are were just more likely to respond in that second poll. And, remember, […]

His varying slopes don’t seem to follow a normal distribution

Bruce Doré writes: I have a question about multilevel modeling I’m hoping you can help with. What should one do when random effects coefficients are clearly not normally distributed (i.e., coef(lmer(y~x+(x|id))) )? Is this a sign that the model should be changed? Or can you stick with this model and infer that the assumption of […]

Bayesian inference completely solves the multiple comparisons problem

I promised I wouldn’t do any new blogging until January but I’m here at this conference and someone asked me a question about the above slide from my talk. The point of the story in that slide is that flat priors consistently give bad inferences. Or, to put it another way, the routine use of […]

Taking Bayesian Inference Seriously [my talk tomorrow at Harvard conference on Big Data]

Mon 22 Aug, 9:50am, at Harvard Science Center Hall A: Taking Bayesian Inference Seriously Over the years I have been moving toward the use of informative priors in more and more of my applications. I will discuss several examples from theory, application, and computing where traditional noninformative priors lead to disaster, but a little bit […]

What’s gonna happen in November?

Nadia Hassan writes: 2016 may be strange with Trump. Do you have any thoughts on how people might go about modeling a strange election? When I asked you about predictability and updating election forecasts, you stated that models that rely on polls at different points should be designed to allow for surprises. You have touted […]

Tax Day: The Birthday Dog That Didn’t Bark

Following up on Valentine’s Day and April Fools, a journalist was asking about April 15: Are there fewer babies born on Tax Day than on neighboring days? Let’s go to the data: These are data from 1968-1988 so it would certainly be interesting to see new data, but here’s what we got: – April 1st […]

Are stereotypes statistically accurate?

Apparently there’s a debate in psychology about the accuracy of stereotypes. Lin Bian and Andrei Cimpian write: In his book Social Perception and Social Reality, Lee Jussim suggests that people’s beliefs about various groups (i.e., their stereotypes) are largely accurate. We unpack this claim using the distinction between generic and statistical beliefs—a distinction supported by […]