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Archive of posts filed under the Bayesian Statistics category.

2018: How did people actually vote? (The real story, not the exit polls.)

Following up on the post that we linked to last week, here’s Yair’s analysis, using Mister P, of how everyone voted. Like Yair, I think these results are much better than what you’ll see from exit polls, partly because the analysis is more sophisticated (MRP gives you state-by-state estimates in each demographic group), partly because […]

Watch out for naively (because implicitly based on flat-prior) Bayesian statements based on classical confidence intervals! (Comptroller of the Currency edition)

Laurent Belsie writes: An economist formerly with the Consumer Financial Protection Bureau wrote a paper on whether a move away from forced arbitration would cost credit card companies money. He found that the results are statistically insignificant at the 95 percent (and 90 percent) confidence level. But the Office of the Comptroller of the Currency […]

My two talks in Austria next week, on two of your favorite topics!

Innsbruck, 7 Nov 2018: The study of American politics as a window into understanding uncertainty in science We begin by discussing recent American elections in the context of political polarization, and we consider similarities and differences with European politics. We then discuss statistical challenges in the measurement of public opinion: inference from opinion polls with […]

What does it mean to talk about a “1 in 600 year drought”?

Patrick Atwater writes: Curious to your thoughts on a bit of a statistical and philosophical quandary. We often make statements like this drought was a 1 in 400 year event but what do we really mean when we say that? In California for example there was an oft repeated line that the recent historic drought was […]

Fitting the Besag, York, and Mollie spatial autoregression model with discrete data

Rudy Banerjee writes: I am trying to use the Besag, York & Mollie 1991 (BYM) model to study the sociology of crime and space/time plays a vital role. Since many of the variables and parameters are discrete in nature is it possible to develop a BYM that uses an Integer Auto-regressive (INAR) process instead of […]

He had a sudden cardiac arrest. How does this change the probability that he has a particular genetic condition?

Megan McArdle writes: I have a friend with a probability problem I don’t know how to solve. He’s 37 and just keeled over with sudden cardiac arrest, and is trying to figure out how to assess the probability that he has a given condition as his doctors work through his case. He knows I’ve been […]

Limitations of “Limitations of Bayesian Leave-One-Out Cross-Validation for Model Selection”

“If you will believe in your heart and confess with your lips, surely you will be saved one day” – The Mountain Goats paraphrasing Romans 10:9 One of the weird things about working with people a lot is that it doesn’t always translate into multiple opportunities to see them talk.  I’m pretty sure the only […]

Stan on the web! (thanks to RStudio)

This is big news. Thanks to RStudio, you can now run Stan effortlessly on the web. So you can get started on Stan without any investment in set-up time, no need to install C++ on your computer, etc. As Ben Goodrich writes, “RStudio Cloud is particularly useful for Stan tutorials where a lot of time […]

Why are functional programming languages so popular in the programming languages community?

Matthijs Vákár writes: Re the popularity of functional programming and Church-style languages in the programming languages community: there is a strong sentiment in that community that functional programming provides important high-level primitives that make it easy to write correct programs. This is because functional code tends to be very short and easy to reason about […]

Podcast interview on polling (mostly), also some Bayesian stuff

Hugo Bowne-Anderson interviewed me for a DataCamp podcast. Transcript is here.

Bayesian inference and religious belief

We’re speaking here not of Bayesianism as a religion but of the use of Bayesian inference to assess or validate the evidence regarding religious belief, in short, the probability that God !=0 or the probability that the Pope is Catholic or, as Tyler Cowen put it, the probability that Lutheranism is true. As a statistician […]

Cool postdoc position in Arizona on forestry forecasting using tree ring models!

Margaret Evans sends in this cool job ad: Two-Year Post Doctoral Fellowship in Forest Ecological Forecasting, Data Assimilation A post-doctoral fellowship is available in the Laboratory of Tree-Ring Research (University of Arizona) to work on an NSF Macrosystems Biology-funded project assimilating together tree-ring and forest inventory data to analyze patterns and drivers of forest productivity […]

Postdoc position: Stan and composite mechanistic and data-driven models of cellular metabolism

Very cool project and possibility to work 3 years developing Stan and collaborating with me (Aki) and other Stan development team. Deadline for applications is 22 October. Quantitative Modelling of Cell Metabolism (QMCM) group headed by Professor Lars Keld Nielsen at DTU, Copenhagen, is looking for experienced Bayesian statistician for a postdoc position. Group specializes […]

Using Stacking to Average Bayesian Predictive Distributions (with Discussion)

I’ve posted on this paper (by Yuling Yao, Aki Vehtari, Daniel Simpson, and myself) before, but now the final version has been published, along with a bunch of interesting discussions and our rejoinder. This has been an important project for me, as it answers a question that’s been bugging me for over 20 years (since […]

N=1 survey tells me Cynthia Nixon will lose by a lot (no joke)

Yes, you can learn a lot from N=1, as long as you have some auxiliary information. The other day I was talking with a friend who’s planning to vote for Andrew Cuomo in the primary. What about Cynthia Nixon? My friend wasn’t even considering voting for her. Now, my friend is, I think, in the […]

Discussion of effects of growth mindset: Let’s not demand unrealistic effect sizes.

Shreeharsh Kelkar writes: As a regular reader of your blog, I wanted to ask you if you had taken a look at the recent debate about growth mindset [see earlier discussions here and here] that happened on theconversation.com. Here’s the first salvo by Brooke McNamara, and then the response by Carol Dweck herself. The debate […]

Against Arianism 2: Arianism Grande

“There’s the part you’ve braced yourself against, and then there’s the other part” – The Mountain Goats My favourite genre of movie is Nicole Kidman in a questionable wig. (Part of the sub-genre founded by Sarah Paulson, who is the patron saint of obvious wigs.) And last night I was in the same room* as […]

“Dynamically Rescaled Hamiltonian Monte Carlo for Bayesian Hierarchical Models”

Aki points us to this paper by Tore Selland Kleppe, which begins: Dynamically rescaled Hamiltonian Monte Carlo (DRHMC) is introduced as a computationally fast and easily implemented method for performing full Bayesian analysis in hierarchical statistical models. The method relies on introducing a modified parameterisation so that the re-parameterised target distribution has close to constant […]

StanCon 2018 Helsinki tutorial videos online

StanCon 2018 Helsinki tutorial videos are now online at Stan YouTube channel List of tutorials at StanCon 2018 Helsinki Basics of Bayesian inference and Stan, parts 1 + 2, Jonah Gabry & Lauren Kennedy Hierarchical models, parts 1 + 2, Ben Goodrich Stan C++ development: Adding a new function to Stan, parts 1 + 2, […]

Hey—take this psychological science replication quiz!

Rob Wilbin writes: I made this quiz where people try to guess ahead of time which results will replicate and which won’t in order to give then a more nuanced understanding of replication issues in psych. Based on this week’s Nature replication paper. It includes quotes and p-values from the original study if people want […]