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Archive of posts filed under the Bayesian Statistics category.

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 […]

Boostrapping your posterior

Demetri Spanos writes: I bumped into your paper with John Carlin, Beyond Power Calculations, and encountered your concept of the hypothetical replication of the point estimate. In my own work I have used a similarly structured (but for technical reasons, differently motivated) concept which I have informally been calling the “consensus posterior.” Specifically, supposing a […]

Smooth poll aggregation using state-space modeling in Stan, from Jim Savage

Jim Savage writes: I just saw your post on poll bounces; have been thinking the same myself. Why are the poll aggregators so jumpy about new polls? Annoyed, I put together a poll aggregator that took a state-space approach to the unobserved preferences; nothing more than the 8 schools (14 polls?) example with a time-varying […]

The p-value is a random variable

Sam Behseta sends along this paper by Laura Lazzeroni, Ying Lu, and Ilana Belitskaya-Lévy, who write: P values from identical experiments can differ greatly in a way that is surprising to many. The failure to appreciate this wide variability can lead researchers to expect, without adequate justification, that statistically significant findings will be replicated, only […]

Guy Fieri wants your help! For a TV show on statistical models for real estate

I got the following email from David Mulholland: I’m a producer at Citizen Pictures where we produce Food Network’s “Diners, Dives and Drive-Ins” and Bravo’s digital series, “Going Off The Menu,” among others. A major network is working with us to develop a show that pits “data” against a traditional real estate agent to see […]

Amazon NYC decision analysis jobs

Dean Foster writes: Amazon is having a hiring event (Sept 8/9) here in NYC. If you are interested in working on demand forecasting either here in NYC or in Seattle send your resume to rcariapp@amazon.com by September 1st, 2016. Here’s the longer blurb: Amazon Supply Chain Optimization Technologies (SCOT) builds systems that automate decisions in […]

All maps of parameter estimates remain misleading

Roland Rau writes: After many years of applying frequentist statistical methods in mortality research, I just began to learn about the application of Bayesian methods in demography. Since I also wanted to change a part of my research focus on spatial models, I discovered your 1999 paper with Phil Price, All maps of parameter estimates […]

Fish cannot carry p-values

Following up on our discussion from last week on inference for fisheries, Anders Lamberg writes: Since I first sent you the question, there has been a debate here too. In the discussion you send, there is a debate both about the actual sampling (the mathematics) and about more the practical/biological issues. How accurate can farmed […]

Bayesian Inference with Stan for Pharmacometrics Class

Bob Carpenter, Daniel Lee, and Michael Betancourt will be teaching the 3-day class starting on 19 September in Paris. Following is the outline for the course: Day 1 Introduction to Bayesian statistics Likelihood / sampling distributions Priors, Posteriors via Bayes’s rule Posterior expectations and quantiles Events as expectations of indicator functions Introduction to Stan Basic […]

When do statistical rules affect drug approval?

Someone writes in: I have MS and take a disease-modifying drug called Copaxone. Sandoz developed a generic version​ of Copaxone​ and filed for FDA approval. Teva, the manufacturer of Copaxone, filed a petition opposing that approval (surprise!). FDA rejected Teva’s petitions and approved the generic. My insurance company encouraged me to switch to the generic. […]

Going beyond confidence intervals

Anders Lamberg writes: In an article by Tom Sigfried, Science News, July 3 2014, “Scientists’ grasp of confidence intervals doesn’t inspire confidence” you are cited: “Gelman himself makes the point most clearly, though, that a 95 percent probability that a confidence interval contains the mean refers to repeated sampling, not any one individual interval.” I […]

Bayesian Linear Mixed Models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

This article by Tanner Sorensen, Sven Hohenstein, and Shravan Vasishth might be of interest to some of you.

Moving statistical theory from a “discovery” framework to a “measurement” framework

Avi Adler points to this post by Felix Schönbrodt on “What’s the probability that a significant p-value indicates a true effect?” I’m sympathetic to the goal of better understanding what’s in a p-value (see for example my paper with John Carlin on type M and type S errors) but I really don’t like the framing […]