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Ed Jaynes outta control!

A commmenter points to a chapter of E. T. Jaynes’s book on probability and inference that contains the following amazing bit: The information we get from the TV evening news is not that a certain event actually happened in a certain way it is that some news reporter has claimed that it did. Even seeing […]

Popper and Jaynes

Deborah Mayo quotes me as saying, “Popper has argued (convincingly, in my opinion) that scientific inference is not inductive but deductive.” She then follows up with: Gelman employs significance test-type reasoning to reject a model when the data sufficiently disagree. Now, strictly speaking, a model falsification, even to inferring something as weak as “the model […]


Aleks points me to this set of commentary on E. T. Jaynes’s book on Bayesian inference. Although I don’t see Jaynes as a guru, I’ve been strongly influenced by his ideas (in particular the idea of taking a model seriously–not as a belief or set of betting probabilities, but as a scientific model–a necessarily oversimplified […]

Jaynes is no guru

E. T. Jaynes was a physicist who applied Bayesian inference to problems in statistical mechanics and signal processing. He was an excellent writer with a dramatic style, and some of his work inspired me greatly. In particular, I like his approach of assuming a strong model and then fixing it when it does not fit […]

Stasi’s back in town. (My last post on Cass Sunstein and Richard Epstein.)

OK, I promise, this will be the last Stasi post ever. tl;dr: This post is too long. Don’t read it.

Several reviews of Deborah Mayo’s new book, Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars

A few months ago I sent the following message to some people: Dear philosophically-inclined colleagues: I’d like to organize an online discussion of Deborah Mayo’s new book. The table of contents and some of the book are here at Google books, also in the attached pdf and in this post by Mayo. I think that […]

Back to the Wall

Jim Windle writes: Funny you should blog about Jaynes. Just a couple of days ago I was looking for something in his book’s References/Bibliography (it along with “Godel, Escher, Bach” and “Darwin’s Dangerous Idea” have bibliographies which I find not just useful but entertaining), and ran across something I wanted to send you but I […]

Aki’s favorite scientific books (so far)

A month ago I (Aki) started a series of tweets about “scientific books which have had big influence on me…”. They are partially in time order, but I can’t remember the exact order. I may have forgotten some, and some stretched the original idea, but I can recommend all of them. I have collected all […]


I’m getting my computer repaired and so will be offline for a few days, won’t be reading or sending email or reading blog comments. The blog will auto-post, though, one per day, with already-scheduled material: “How to Assess Internet Cures Without Falling for Dangerous Pseudoscience” Ed Jaynes outta control! A reporter sent me a Jama […]

The house is stronger than the foundations

Oliver Maclaren writes: Regarding the whole ‘double use of data’ issue with posterior predictive checks [see here and, for a longer discussion, here], I just wanted to note that David Cox describes the ‘Fisherian reduction’ as (I’ve summarised slightly; see p. 24 of ‘Principles of Statistical Inference) – Find the likelihood function – Reduce to […]

On deck through the rest of the year (and a few to begin 2018)

Here they are. I love seeing all the titles lined up in one place; it’s like a big beautiful poem about statistics: After Peptidegate, a proposed new slogan for PPNAS. And, as a bonus, a fun little graphics project. “Developers Who Use Spaces Make More Money Than Those Who Use Tabs” Question about the secret […]

Bayesian statistics: What’s it all about?

Kevin Gray sent me a bunch of questions on Bayesian statistics and I responded. The interview is here at KDnuggets news. For some reason the KDnuggets editors gave it the horrible, horrible title, “Bayesian Basics, Explained.” I guess they don’t waste their data mining and analytics skills on writing blog post titles! That said, I […]

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 […]

“The Bayesian Second Law of Thermodynamics”

Someone pointed me to this paper (by Anthony Bartolotta, Sean Carroll, Stefan Leichenauer, and Jason Pollack) and asked me what I thought. I didn’t have the time to look into it in any detail, but based on the title it seemed a bit Jaynesian. I sent it to a statistician and former physicist, who wrote: […]

Some general principles of Bayesian data analysis, arising from a Stan analysis of John Lee Anderson’s height

God is in every leaf of every tree. The leaf in question today is the height of journalist and Twitter aficionado Jon Lee Anderson, a man who got some attention a couple years ago after disparaging some dude for having too high a tweets-to-followers ratio. Anderson called the other guy a “little twerp” which made […]

The blogroll

I encourage you to check out our linked blogs. Here’s what they’re all about: Cognitive and Behavioral Science BPS Research Digest: I haven’t been following this one recently, but it has lots of good links, I should probably check it more often. There are a couple things that bother me, though. The blog is sponsored […]

Everyone’s trading bias for variance at some point, it’s just done at different places in the analyses

Some things I respect When it comes to meta-models of statistics, here are two philosophies that I respect: 1. (My) Bayesian approach, which I associate with E. T. Jaynes, in which you construct models with strong assumptions, ride your models hard, check their fit to data, and then scrap them and improve them as necessary. […]

Ways of knowing

In this discussion from last month, computer science student and Judea Pearl collaborator Elias Barenboim expressed an attitude that hierarchical Bayesian methods might be fine in practice but that they lack theory, that Bayesians can’t succeed in toy problems. I posted a P.S. there which might not have been noticed so I will put it […]

Examples of the use of hierarchical modeling to generalize to new settings

In a link to our back-and-forth on causal inference and the use of hierarchical models to bridge between different inferential settings, Elias Bareinboim (a computer scientist who is working with Judea Pearl) writes: In the past week, I have been engaged in a discussion with Andrew Gelman and his blog readers regarding causal inference, selection […]

The Naval Research Lab

I worked at the U.S. Naval Research Laboratory for four summers during high school and college. I spent much of my time writing a computer program to do thermal analysis for an experiment that we put on the space shuttle. The facility I developed with the finite-element method came in handy in my job at […]