From Josh Wills at Cloudera, a post on reservoir sampling.

Stephen Wolfram (and presumably part of his army of people working for him) have some interesting visualizations of Data Science of the Facebook world.

Brian Hayes maps the Hilbert curve.

Dana Mackenzie at Slate writes on the mathematics of jury sizes. Also at Slate, Phil Plait writes for the Bad Astronomy blog on the analemma.

How to sort comments intelligently and this post on Bayesian methods for multi-armed bandits are part of Cam Davidson-Pilon’s book Probabilistic Programming and Bayesian Methods for Hackers. I found Davidson-Pilon via his list of machine learning counterexamples.

Kenneth Appel (of four colors suffice fame) died.

Jane Austen as a game theorist

Brian Hayes introduces streaming algorithms via the Britney Spears problem. Separately he introduces quantum computing.

Allen Downey takes a Bayesian approach to the Price Is Right problem.

by Jonathan Borwein, The life of pi: from Archimedes to ENIAC and beyond.

Today’s Google Doodle honors Leonhard Euler, for his 306th birthday.

Some news coverage, quickly gathered from Google News: Time, Guardian, National Geographic, Huffington Post, Entertainment Weekly, Times of India, Telegraph, NDTV, and with happy shiny video of Euler’s Disk, slate.fr.

Unreliable neuroscience? Why power matters, by Kate Button at the Guardian.

Ghost Leg is an interesting method for generating permutations at random.

John Cook on social networks in fact and fiction.

And because taxes are due today in the United States, if the ITS discovered the quadratic formula.

At Cross Validated, someone asked about why they get wildly different histograms from the same data. The user Glen_b gave an excellent answer based around an example for which data sets which differ from each other just by adding a constant have very different-looking histograms. Other commenters suggest using kernel density estimates or cumulative distribution plots, both of which wouldn’t fail on this particular question.

Anscombe’s quartet comes to mind – four bivariate data sets with the same mean and variance of each coordinate and the same correlation, which look wildly different when plotted. This is sort of a reverse-Anscombe: here data sets that look essentially the same when plotted have wildly different summary statistics.

From metafilter, mesmerizing visualizations of genetic algorithms.

The paper and pencil cosmological calculator.

Zipfian Academy is offering to train people to become data scientists in twelve intense weeks. (via.)

A prize is on offer for improving prediction of flight delays..

Sebastian Bubeck’s blog on “topics in optimization, probability, and statistics.

A tumblr of transit maps . (Yes, not really about math -b ut sort of tickles the same part of the brain, no?)

E. O. Wilson on why scientists don’t need math, and Jeremy Fox on why they do.

Tonight the God Plays Dice art department made blondies!

These are supposed to be made, according to the recipe, in a pan which is an eight-inch square. But we have no such thing. We do have a nine-inch circular pan, though. Will that do?

Well, what matters is that the two pans have the same area – and therefore that the same volume of batter will have the same thickness and cook roughly the same. (If you thought I was going to solve some PDEs and work out how the heat transfers, you haven’t been paying attention.)

A nine-inch circle has area $\pi (9/2)^2 = 81\pi/4$ square inches, which is about 63.62. An eight-inch square, of course, has area 64 square inches. Not bad!

What would it take for this approximation to be exactly correct? This would require that $81\pi/4 = 64$ exactly; solving for $\pi$ gives \$\pi = 256/81″, which is often credited as an Egyptian approximation to $\pi$ as it implicitly appears in the Rhind papyrus, an ancient Egyptian document of,problems in mathematics. In fact the setting in which this is established there is almost exactly this one – a circle of diameter 9 and a square of side 8 are said to have the same area. See for example these slides for a history of math class by Bill Cherowitzo.

This isn’t the greatest approximation of $\pi$ – in fact $81\pi$ is about 254.46 – but it has the added “virtue” that 256 is a power of two, and 81 is a power of three. We could write $\pi \approx 2^8/3^4$ – it looks nicer that way, I think.

And because Internet law forbids me from mentioning food without posting a picture of it: