I already blogged about Bayes’s Theorem application here. However I found this other introduction very interristing. The theorem is summarized by:

Bayes’ theorem is the formula that calculates these updated probabilities. Using H to stand for a hypothesis (such as H₁, H₂ or H₃), and E a piece of evidence (such as Result A, Result B, or Result C), it says: P(H|E) = P(H)*P(E|H)/P(E)  

The Monty Hall problem graphical solution is also very good (Solution is after the jump):

The situation is this: you’re a contestant on a game show seeking to win a car. Before you are three doors, one of which contains a car, and the other two of which contain goats. You will make an initial "guess" at which door contains the car — that is, you will select one of the doors, without opening it. At that point, the host will open a goat-containing door from among the two that you did not select. You will then have to decide whether to stick with your original guess and open the door that you originally selected, or switch your guess to the remaining unopened door. The question is whether it is to your advantage to switch — that is, whether the car is more likely to be behind the remaining unopened door than behind the door you originally guessed.

Nowadays we check weather forecast on TV, on the Internet or even using a WII. But if we were alone in an island, the tips described in this wikihow article would be useful.

Found via haha.

In the TED presentation below, Dr Dean Ornish provides some insight about the rise of obesity, diabetes and cardiovascular diseases accros the globe.

Mark C. Chu-Carroll  of Good Math published an interesting post on “C” language efficiency. He demonstrates that C or C++ are not the more efficient languages for numerical calculations like computational fluid dynamics. He takes the alias detection example: C or C++ compiler are unable to identify if two variables might be referencing the same location. Whereas other languages like FORTRAN or OCaml. This leads to better calculation’s optimization.