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Archive of posts filed under the Bayesian Statistics category.

Stochastic natural-gradient EP

Yee Whye Teh sends along this paper with Leonard Hasenclever, Thibaut Lienart, Sebastian Vollmer, Stefan Webb, Balaji Lakshminarayanan, and Charles Blundell. I haven’t read it in detail but they not similarities to our “expectation propagation as a way of life” paper. But their work is much more advanced than ours.

“if you add a few more variables, you can do a better job at predictions”

Ethan Bolker points me to this news article by Neil Irwin: Robert J. Gordon, an economist at Northwestern University, has his own version that he argues explains inflation levels throughout recent decades. But it is hardly simple. Its prediction for inflation relies not just on joblessness but also on measures of productivity growth, six shifts […]

David MacKay

I learned from this comment that David MacKay has passed away. Here’s an obituary, which has a lot of information, really much more than I could give because I only met MacKay a couple of times. The first time was when I was in Cambridge, England, for a conference, and I got there a day […]

Avoiding model selection in Bayesian social research

The other day I happened to come across this paper that I wrote with Don Rubin in 1995. I really like it—it’s so judicious and mature, I can’t believe I wrote it over 20 years ago! Let this be a lesson to all of you that it’s possible to get somewhere by reasoning from first […]

Bayesian Umpires: The coolest sports-statistics idea since the hot hand!

Hiro Minato points us to this recent article by Guy Molyneux: Baseball fans have long known, or at least suspected, that umpires call balls and strikes differently as the count changes. At 0-2, it seems that almost any taken pitch that is not right down the middle will be called a ball, while at 3-0 […]

Why I don’t believe Fergus Simpson’s Big Alien Theory

It all began with this message from Christopher Bonnett: I’m a observational cosmologist and I am writing you as I think the following paper + article might be of interest for your blog. A fellow cosmologist, Fergus Simpson, has done a Bayesian analysis on the size of aliens, it has passed peer-review and has been […]

“A strong anvil need not fear the hammer”

Wagenmakers et al. write: A single experiment cannot overturn a large body of work. . . . An empirical debate is best organized around a series of preregistered replications, and perhaps the authors whose work we did not replicate will feel inspired to conduct their own preregistered studies. In our opinion, science is best served […]

“Bayesians (quite rightly so according to the theory) . . .”

Stephen Senn writes, “Bayesians (quite rightly so according to the theory) have every right to disagree with each other.” He could also add, “Non-Bayesians (quite rightly so according to the theory) have every right to disagree with each other.” Non-Bayesian statistics, like Bayesian statistics, uses models (or, if you prefer, methods). Different researchers will use […]

He’s looking for Bayesian time-series examples

Maurits Van Wagenberg writes: Coming from the traditional side, started to use Bayes, quickly limiting it to models with less variables, notwithstanding the lure. Am not in academics but have for many years researched design processes of complex objects such as engineering complex process plants. These processes have a lead-time from 12 to 18 months. […]

Data-dependent prior as an approximation to hierarchical model

Andy Solow writes: I have a question about Bayesian statistics. Why is it wrong to use the same data to formulate the prior and to update it to the posterior? I am having a hard time coming up with – or finding in the literature – a formal reason. I asked him to elaborate and […]

Thinking about this beautiful text sentiment visualizer yields a surprising insight about statistical graphics

Lucas Estevem set up this website in d3 as his final project in our statistical communication and graphics class this spring. Copy any text into the window, push the button, and you get this clean and attractive display showing the estimated positivity or negativity of each sentence. The length of each bar is some continuously-scaled […]

Noise noise noise noise noise

An intersting issue came up in comments to yesterday’s post. The story began with this query from David Shor: Suppose you’re conducting an experiment on the effectiveness of a pain medication, but in the post survey, measure a large number of indicators of well being (Sleep quality, self reported pain, ability to get tasks done, […]

Actually, I’d just do full Bayes

Dave Clark writes: I was hoping for your opinion on a topic related to hierarchical models. I am an actuary and have generally worked with the concept of hierarchical models in the context of credibility theory. The text by Bühlmann and Gisler (A Course in Credibility Theory; Springer) sets up the mixed models under the […]

I definitely wouldn’t frame it as “To determine if the time series has a change-point or not.” The time series, whatever it is, has a change point at every time. The question might be, “Is a change point necessary to model these data?” That’s a question I could get behind.

From the Stan users list: I’m trying out to fit a time series that can have 0 or 1 change point using the sample model from Ch 11.2 of the manual. To determine if the time series has a change-point or not, would I need to do comparison model (via loo) between 1-change model developed […]

Stan Case Studies Launches

There’s a new section of the Stan web site, with case studies meant to illustrate statistical methodologies, classes of models, application areas, statistical computation, and Stan programming. Stan Case Studies The first ten or so are up, including a grab bag of education models from Daniel Furr at U.C. Berkeley: Hierarchical Two-Parameter Logistic Item Response […]

Swimsuit special: “A pure Bayesian or pure non-Bayesian is not forever doomed to use out-of-date methods, but at any given time the purist will be missing some of the most effective current techniques.”

Joshua Vogelstein points me to this paper by Gerd Gigerenzer and Julian Marewski, who write: The idol of a universal method for scientific inference has been worshipped since the “inference revolution” of the 1950s. Because no such method has ever been found, surrogates have been created, most notably the quest for significant p values. This […]

Lack of free lunch again rears ugly head

We had some discussion on blog the other day of prior distributions in settings such as small experiments where available data do not give a strong inference on their own, and commenter Rahul wrote: In real settings I rarely see experts agree anywhere close to a consensus about the prior. Estimates are all over the […]

How should statisticians and economists think about recreational gambling?

Recreational gambling is a lot like recreational drinking, in that it is pleasant, and it can be abused, and the very aspects that make it pleasant are related to what makes it so destructive when abused. Also, both industries make a lot of money, so there’s a continuing tug of war between those who sell […]

The unbelievable reason that Jennifer Lawrence is using Waic and cross-validation for survival models

Sam Brilleman writes: I’ve been reading two of your recent papers: (1) Gelman A, Hwang J, Vehtari A. Understanding predictive information criteria for Bayesian models. Statistics and Computing 2014; 24: 997-1016. (2) Vehtari A, Gelman A. WAIC and cross-validation in Stan. Submitted. 2014. http://www.stat.columbia.edu/~gelman/research/unpublished/waic_stan.pdf. Accessed: 6 July 2015. My question in short is: The example […]

0.05 is a joke

Jim Delaney points to this tutorial by F. Perry Wilson on why the use of a “p less than 0.05” threshold does not imply a false positive rate of 5%, even if all the assumptions of the model are true. This is standard stuff but it’s always good to see it one more time. Delaney […]