http://online.wsj.com/article/SB10001424127887323372504578466992305986654.html

Apparently a hedge fundie made 4 billion and is giving most of it away to what the WSJ describes as a "moneyball" approach to giving.

http://pipeline.corante.com/archives/2012/11/27/how_do_chemist_think_that_they_judge_compounds.php

Some background: medicinal chemists are responsible for identifying drug candidates, usually by screening large (10^6) libraries of combinatorially generated molecules. Some of these hits turn out to be biologically active, and then it's up to the medicinal chemists to decide whether these hits are false positives or not, and further, to synthesize analogous compounds to see if they can tweak the biological activity of each compound.

It's in this 'synthesizing analogous compounds' step that subjective judgment comes in, with Lipinski's rule of five being the most 'basic' of the heuristics, and with most medicinal chemists adopting more and more complex heuristics. Or,  as this paper shows, perhaps they're just deluding themselves and their true metric is something very simple, and after making their decision, they dress it up with fancy post-hoc rationalizations.

http://xkcd.com/1132/

Is this a fair representation of frequentists versus bayesians? I feel like every time the topic comes up, 'Bayesian statistics' is an applause light for me, and I'm not sure why I'm supposed to be applauding.

"In 2005, Dr. Zhang was having an ongoing discussion with friends about the Lottery, with Dr. Zhang taking the view that it offered poor odds and was a tax mainly on poor people. To bolster his argument, he began analyzing the Massachusetts Lottery’s various games. But when he got to Cash WinFall, he was shocked to find that during roll-down drawings the odds were in the bettor’s favor."

Full story here - it's rather engrossing.

http://www.mass.gov/ig/publications/reports-and-recommendations/2012/lottery-cash-winfall-letter-july-2012.pdf

(Cross-posted from my blag)

1. My microwave clock has been broken since a power outage. It now reads something like 7 hours behind the real time. I've been too lazy to fix it, but the bright side is that it's 7 hours behind, and not 15 minutes behind. If it were 15 minutes behind, then who knows - I might mistake it to be the correct time, and end up fifteen minutes late to an appointment.

2. During the early years of computing, scientists and engineers were responsible for running numerical simulations of various sorts. To do this, they needed randomized starting conditions. Von Neumann favored the "middle square" method. While this method was not a very good pseudo-random number generator, its speed made up for its shortcomings in those early days. Additionally, a useful property of the middle-square method was that when it fell into a short cycle, it was immediately obvious. While other methods may have had undetectable cycles of intermediate length, the middle-square method would invariably output legitimate pseudorandom numbers for some time, then fall into a cycle of length 1, 2 or 4. (1)

3. One day, I was playing with some traffic models. My goal was to be able to correctly model the behavior of a line of cars as they accelerated from a standstill when the light turned from red to green. (2) I had collected some actual data at an intersection, and was planning to test my model against the data, as well as to fit some parameters. I ran a short program to fit these parameters and plot the actual times vs. my program's predicted times. To my astonishment, I had almost a perfect fit! Upon deeper inspection, it turned out that my program had merely gotten the times right by coincidence - while the cars behaved nicely long enough to get to the intersection, afterwards, their velocities oscillated with exponentially growing amplitude. I would have missed this if I had not insisted on checking the raw numbers from the simulation. I recoded my simulation and got a worse fit, but at least it didn't blow up as it had before.

(This last story is fiction. Or so, I hope)

4. The US News and World Report was doing its annual ranking of universities. They had recently changed the weightings on some of the subscores. Upon running their algorithm, an unexpected candidate rose to the top - Caltech! (Zing.) They concluded that there must have been a mistake with their algorithm, readjusted their weightings, and reran their algorithm. This time, Harvard rose to the top, as it should have. Happy with their results, US News and World Report published their university rankings and raked in a lot of dough.

Notes:

(1) Test the middle-square story. Is it actually true that all cycles are short? My code, if you want to play with it.

(2) The time for the nth car to reach the intersection is about 2*n seconds

(I wrote this post for my own blog, and given the warm reception, I figured it would also be suitable for the LW audience. It contains some nicely formatted equations/tables in LaTeX, hence I've left it as a dropbox download.)

Logarithmic probabilities have appeared previously on LW here, here, and sporadically in the comments. The first is a link to a Eliezer post which covers essentially the same material. I believe this is a better introduction/description/guide to logarithmic probabilities than anything else that's appeared on LW thus far.

Introduction:

Our conventional way of expressing probabilities has always frustrated me. For example, it is very easy to say nonsensical statements like, “110% chance of working”. Or, it is not obvious that the difference between 50% and 50.01% is trivial compared to the difference between 99.98% and 99.99%. It also fails to accommodate the math correctly when we want to say things like, “five times more likely”, because 50% * 5 overflows 100%.
Jacob and I have (re)discovered a mapping from probabilities to log- odds which addresses all of these issues. To boot, it accommodates Bayes’ theorem beautifully. For something so simple and fundamental, it certainly took a great deal of google searching/wikipedia surfing to discover that they are actually called “log-odds”, and that they were “discovered” in 1944, instead of the 1600s. Also, nobody seems to use log-odds, even though they are conceptually powerful. Thus, this primer serves to explain why we need log-odds, what they are, how to use them, and when to use them.

Article is here (Updated 11/30 to use base 10)