> a hint, you cannot disregard something merely because it's implausible Coming back to Bayesian statistics, the word for this is prior, and its not that you d

> a hint, you cannot disregard something merely because it's implausible Coming back to Bayesian statistics, the word for this is prior, and its not that you disregard evidence, its that you can quantify both your existing beliefs about reality and the change in your beliefs according to the evidence you see. Firstly, our hint: we have a prior assumption of the probability that the popularity of Facebook is driving up Greek debt (call it P(Fg)). Then, we observe a correlation between these two things. For the sake of argument, I'm going to make this 0.001 (I'd probably estimate less). Now, once we see this correlation, we now need to calculate two things: 1. The probability of observing that correlation (call that P(C)). Note that the more extraordinary the correlation, the less probable it is, and the smaller this term would be. In this case, the graph matches vaguely, I'm going to give it a probability of 0.1. 2. Given a world where there is a causation, what is the probability we'd see this correlation (Q: I'm not 100% on this part). Now, Greek debt could plausibly be driven by other things, which would mask the Facebook effect, so there's no guarantee there would be a correlation. This term is called P(C | Fg), and I have no idea what value to give it. Let's try 0.5. What we want to know is: P(Fg | C), that is, the probability of a connection given we have observed a correlation. Boom! P(Fg | C) = P(C | Fg) x P(Fg) / P(C) So our posterior probability (after observing this correlation) changes from 0.001 to 0.001 x 0.5 / 0.1 = 0.005