Vacation and Training

I hit the road until mid may, coming back with more architecture related topics.

Unmasking the Beard

I trained my Bayesian Network modeling skills with a sportsbetting fraud. We call it a beard, when a professional is masking his insider abuse by placing his bets with help of an acquaintance. It will be in nearly all cases a relative or a friend. How can we imply from evidence of surname to being relative? And then from being relative to being "beard?" Or: how can we imply friendship, and then beard?

The naive model presented shows reasonable behaviour.

First the presentation of the marginal probalilities (the numbers) and the inference sensitivity (red boxes) of nodes

Being a beard can only happen if there is a pro to mask. So this evidence remains set. Friends and relatives highly influence the other nodes because of their outgoing edges and their prior conditional probabilities. Interestingly being from same district has also a high impact because its relatively rare measured within a nation. We see, being from the same district increases beard probability +3% in this model:

Download and play it with GeNie.

A screencast:

Preview of Wolfram Alpha

You can today test a similiar system to the pre announced Wolfram Alpha and get a feeling of the potential. Ask quantitative questions like: How many legs has a chair? How old is Michael Jackson? Now think that WA will actually try to compute stuff: how much bigger is the GDP of US to china? I'm excited!

Regression Towards the Mean

The psychologist Daniel Kahneman referred to regression to the mean in his speech when he won the 2002 Bank of Sweden prize for economics.

“

I had the most satisfying Eureka experience of my career while attempting to teach flight instructors that praise is more effective than punishment for promoting skill-learning. When I had finished my enthusiastic speech, one of the most seasoned instructors in the audience raised his hand and made his own short speech, which began by conceding that positive reinforcement might be good for the birds, but went on to deny that it was optimal for flight cadets. He said, "On many occasions I have praised flight cadets for clean execution of some aerobatic maneuver, and in general when they try it again, they do worse. On the other hand, I have often screamed at cadets for bad execution, and in general they do better the next time. So please don't tell us that reinforcement works and punishment does not, because the opposite is the case." This was a joyous moment, in which I understood an important truth about the world: because we tend to reward others when they do well and punish them when they do badly, and because there is regression to the mean, it is part of the human condition that we are statistically punished for rewarding others and rewarded for punishing them. I immediately arranged a demonstration in which each participant tossed two coins at a target behind his back, without any feedback. We measured the distances from the target and could see that those who had done best the first time had mostly deteriorated on their second try, and vice versa. But I knew that this demonstration would not undo the effects of lifelong exposure to a perverse contingency.

http://en.wikipedia.org/wiki/Regression_towards_the_mean

Bailout Micropayment

A thought doesn't leave me. Why does no central bank issue iCash? 

A innovation in online payment would lease the liqudity/ loan problem and boost consumer spending. Why not subsidize every computer with a smartcard reader approved for financial transactions?

Don't mix Correlation with Causality

The statistic course or to put it in another way: even if there is a correlation between storks and babys in burgenland, storks are not the cause of their birth.