Banfffffffffffffff

I’ve just arrived in chilly but beautiful Banff for a workshop on Information theory and statistics for large alphabets. I’m looking forward to it, although I will have to miss the last day due to the timing of flights out of Calgary that get me to Chicago before midnight. My itineraries there and back seem especially perverse : ORD-SEA-YYC and YYC-SFO-ORD. However, thanks to the new gig I have a new laptop with a functional battery so I am doing a bit more busy-work and less New Yorker reading in the plane. I might try to write a bit more about the topics in the workshop — although the topic seems focused, there are a wide range of approaches and angles to take on the problem of estimating probabilities/prevalences in situations where you may not get to see each outcome once. Certainly I hope I can get the journal version of a paper from last year’s Allerton squared away.

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Bayesianism in philosophy

In an effort to get myself more philosophically informed with regards to probability and statistics, I’ve been reading about various notions and their discontents, such as symmetry, or Bayesianism, or p-values. I was delighted to find this recent pair of papers (part I,part II) by fellow Berkeley-ite and occasional puzzle-partner Kenny Easwaran (now a prof at USC) on Bayesianism in Philosophy Compass. In the first paper he goes through basic tenets of Bayesian approaches to probability in terms of subjective belief, and their philosophical justification via rational actions or “Dutch book” arguments and representation theorems. What’s also interesting from a scientific view (somewhat off-topic from the article) is the angle being advanced (some might say “pushed”) by some cognitive scientists that people are actually doing some kind of Bayesian conditionalization in certain tasks (here’s a plug for my buddy Pradeep‘s work). The second article talks about the difficulties in developing a consistent and quantitative “confirmation theory” in Bayesianism. In different fields there are different questions how how to do this, and as Kenny points out, the anti-Bayesians in different fields are different — the null-position is not necessarily frequentism.

They’re a relatively quick read, and I think provide some different perspectives for those of us who usually see these concepts in our little fiefdoms.