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Archive of posts filed under the Bayesian Statistics category.

Stan on the beach

This came in the email one day: We have used the great software Stan to estimate bycatch levels of common dolphins (Delphinus delphis) in the Bay of Biscay from stranding data. We found that official estimates are underestimated by a full order of magnitude. We conducted both a prior and likelihood sensitivity analyses : the […]

Nick and Nate and Mark on Leicester and Trump

Just following up on our post the other day on retrospective evaluations of probabilistic predictions: For more on Leicester City, see Nick Goff on Why did bookmakers lose on Leicester? and What price SHOULD Leicester have been? (forwarded to me by commenter Iggy). For more on Trump, see Nate Silver on How I Acted Like […]

Birthday analysis—Friday the 13th update, and some model checking

Carl Bialik and Andrew Flowers at (Nate Silver’s site) ran a story following up on our birthdays example—that time series decomposition of births by day, which is on the cover of the third edition of Bayesian Data Analysis using data from 1968-1988, and which then Aki redid using a new dataset from 2000-2014. Friday […]

Is fraac Scott Adams?

tl;dr: If you value your time, don’t read this post.

Point summary of posterior simulations?

Luke Miratrix writes: ​In the applied stats class ​I’m teaching ​on​ hierarchical models I’m giving the students (a mix of graduate students, many from the education school, and undergrads) a taste of Stan. I have to give them some “standard” way to turn Stan output into a point estimate (though of course I’ll also explain […]

Bill James does model checking

Regular readers will know that Bill James was one of my inspirations for becoming a statistician. I happened to be browsing through the Bill James Historical Baseball Abstract the other day and came across this passage on Glenn Hubbard, who he ranks as the 88th best second baseman of all time: Total Baseball has Glenn […]

Gary Venter’s age-period-cohort decomposition of US male mortality trends

Following up on yesterday’s post on mortality trends, I wanted to share with you a research note by actuary Gary Venter, “A Quick Look at Cohort Effects in US Male Mortality.” Venter produces this graph: And he writes: Cohort effects in mortality tend to be difficult to explain. Often strings of coincidences are invoked – […]

Lots of buzz regarding this postdoc position in London

Tom Churcher writes: We are currently advertising for an infectious disease modeller to investigate the impact of insecticide resistance on malaria control in Africa. The position is for 3 years in the first instance and is funded by the Wellcome Trust. No previous malaria or mosi experience required. Please circulate to anyone who might be […]

What is the “true prior distribution”? A hard-nosed answer.

The traditional answer is that the prior distribution represents your state of knowledge, that there is no “true” prior. Or, conversely, that the true prior is an expression of your beliefs, so that different statisticians can have different true priors. Or even that any prior is true by definition, in representing a subjective state of […]

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 […]