Monthly Archives: October 2012

Are tweets more accurate than science papers?

John Myles White brings up an interesting question on Twitter: Ioannidis thinks most published biological research findings are false. Do you think >50% of tweets are false? I’m inclined to think tweets may be more accurate than research papers, mostly

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Euler characteristic with dice

For any convex solid, V – E + F = 2 where V is the number of vertices, E the number of edges, and F the number of faces. The number 2 in this formula is a topological invariant of

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Posted in Math

Programming miscellany

Here are a few of my favorite programming-related links that I’ve run across lately. Functional Programming with Python NoSQL is dual to SQL Why you would want to program at fifty (or any other age) The Poetry of Function Naming

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Posted in Software development

Product of normal PDFs

The product of two normal PDFs is proportional to a normal PDF. This is well known in Bayesian statistics because a normal likelihood times a normal prior gives a normal posterior. But because Bayesian applications don’t usually need to know

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Posted in Math, Statistics

Make your own buckyball

This weekend a couple of my daughters and I put together a buckyball from a Zometool kit. The shape is named for Buckminster Fuller of geodesic dome fame. Two years after Fuller’s death, scientists discovered that the shape appears naturally

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Ramanujan's most beautiful identity

G. H. Hardy called the following equation Ramanujan’s “most beautiful identity.” For |q| < 1, If I understood it, I might say it’s beautiful, but for now I can only say it’s mysterious. Still, I explain what I can. The

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Posted in Math

Sun, milk, red meat, and least-squares

I thought this tweet from @WoodyOsher was pretty funny. Everything our parents said was good is bad. Sun, milk, red meat … the least-squares method. I wouldn’t say these things are bad, but they are now viewed more critically than

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Posted in Statistics

Python for data analysis

I recommend using Python for data analysis, and I recommend Wes McKinney’s book Python for Data Analysis. I prefer Python to R for mathematical computing because mathematical computing doesn’t exist in a vacuum; there’s always other stuff to do. I

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Posted in Python

Dead man writing

Paul ErdÅ‘s was an extraordinary mathematical collaborator. He traveled constantly, cross-pollinating the mathematical community. He wrote about 1500 papers and had around 500 coauthors. According to Ron Graham, He’s still writing papers, actually. He’s slowed down. Because many people started

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Dimension 5 isn't so special

Lately I’ve been reading The Best Writing on Mathematics 2012. I’d like to present a alternative perspective on one of the articles. In his article “An Adventure in the Nth Dimension,” Brian Hayes explores how in high dimensions, balls have

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Fixing computers

When I was growing up and ordinary people were becoming aware of computers, my father told me that he thought there would be good money in fixing computers when they break down. Looking back on this, it’s obvious why he

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Volatility in adaptive randomization

Randomized clinical trials essentially flip a coin to assign patients to treatment arms. Outcome-adaptive randomization “bends” the coin to favor what appears to be the better treatment at the time each randomized assignment is made. The method aims to treat

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Posted in Clinical trials

Competence and prestige

The phrase “downward nobility” is a pun on “upward mobility.” It usually refers to taking a less lucrative but more admired position. For example, it might be used to describe a stock broker who becomes a teacher in a poor

Posted in Business

Seven John McCarthy papers in seven weeks

I recently ran across a series of articles from Carin Meier going through seven papers by the late computer science pioneer John McCarthy in seven weeks. Published so far: Prologue #1: Ascribing Mental Qualities to Machines #2: Towards a Mathematical

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Posted in Computing

Shifting probability distributions

One reason the normal distribution is easy to work with is that you can vary the mean and variance independently. With other distribution families, the mean and variance may be linked in some nonlinear way. I was looking for a

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Posted in Statistics