Bill Gillespie, of Metrum, is giving a tutorial next week at ACoP: Getting Started with Bayesian PK/PD Modeling Using Stan: Practical use of Stan and R for PK/PD applications Thursday 8 October 2015, 8 AM — 5 PM, Crystal City, VA This is super cool for us, because Bill’s not one of our core developers […]
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message. An explanation follows.
The 10 digits are:
If you missed it the first time around, here’s a link to: Stan Puzzle 1: Inferring Ability from Streaks First, a hat-tip to Mike, who posted the correct answer as a comment. So as not to spoil the surprise for everyone else, Michael Betancourt (different Mike), emailed me the answer right away (as he always […]
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This week I visited Oberlin College to deliver the Fuzzy Vance Lecture in Mathematics. I was honored to be the 20th Fuzzy Vance lecturer. Each year, Oberlin invites one mathematician (or an operations researcher/fake mathematician in my case!) to visit campus, participate in classes, and give a lecture (the “Fuzzy Vance Lecture”) to the general public. My evening […]
Western Washington University, in Bellingham, WADevlin’s Angle for July 2006 was titled Letter to a calculus student. In it, I tried to describe, as briefly but as effectively as I could, the deep beauty there is in calculus, a beauty that arises from the depth of human brilliance that it took for the human mind to find a way to tame the infinite, and bend it to our use, a beauty made the more so by the enormous impact calculus has had on life on Earth.In my essay, I acknowledged […]
From the paper: "...In this paper, we tackle these scalability bottlenecks by focusing on what embeddings are actually used for: computing ℓ2-based pairwise similarity metrics typically used for supervised or unsupervised learning. For example, K-means clustering uses pairwise Euclidean distances, and SVM-based classification uses pairwise inner products. We therefore ask the following question: “Is it possible to compute an embedding which captures the pairwise euclidean distances between
There was a little bit of anxiety in the room at the beginning of class today as it is quiz day. I hate that my students get stressed about a quiz, but I understand it. After returning homework set 14, which I had corrected last night, I gave them 15 minutes to talk to each other about any lingering issues around solving by substitution or elimination. I also answered questions one-on-one. Before starting the quiz I warned them that the last question was unlike any they had done, but that they
Actually the course is called Statistical Communication and Graphics, but I was griping about how few students were taking the class, and someone suggested the title Communicating Data and Statistics as being a bit more appealing. So I’ll go with that for now. I love love love this class and everything that’s come from it […]
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A rocket scientist I know once observed the fundamental similarity between LA's two defining industries: entertainment and aerospace. Both exist from deal to deal, lining up risky, hugely expensive projects. Each of these projects require much of the work to be done from scratch, so much so that it's often like setting up a new business every time a deal goes through.There are, of course, limits to the analogy. In business terms, perhaps the biggest is the nature of the customer. For all […]
Today I’m fascinated by the story described in this three-part American Banker series on the Consumer Financial Protection Bureau’s (CFPB’s) use of disparate impact, written by Rachel Witkowski. Disparate impact, according to the article, is a legal theory that says lenders can be penalized if they have a neutral policy that creates an adverse impact against […]
M27 (also known as the Dumbbell Nebula because of its shape) is a planetary nebula in the constellation Vulpecula, just north of the bright star Altair. My photo: Photo location: Monifieth (near Dundee), Scotland, UK. Date: 1 Oct 2015; 21:00 UTC. Telescope: 11-inch Celestron SCT. Camera: Pentax K3 Exposure: ISO 800, 40 30-second exposures stacked […]
I've been talking to many people about algorithmic fairness of late, and I've realized that at the core of pushback against algorithmic bias ("algorithms are just math! If the code is biased, just look at it and you can fix it !") is a deep misunderstanding of the nature of learning algorithms, and how they differ fundamentally from the traditional idea of an algorithm as "a finite set of well-defined elementary instructions that take an input and produce an output".This misunderstanding is […]