By modeling the accuracy change we can infer the true change point and fraction of data affected by the change (a proxy for detection confidence). The accuracy of this classifier varies for different candidate change points. First, it labels data as before or after a candidate change point and trains a classifier to predict these labels. We describe a self-training model-agnostic framework to detect changes in arbitrarily complex data. So the chances of getting 2 tails and 1 heads in three flips is 4/9, or about 44%.Change point detection has many practical applications, from anomaly detection in data to scene changes in robotics however, finding changes in high dimensional data is an ongoing challenge. Now that I've demonstrated that the equation works, you can substitute any probability in for p and q, as long as they add up to 1. 375, which is equal to 3/8, same as before Since you want 2 tails and 1 head, you choose the one that includes pq^2.ģ(1/2)(1/2)^2 =. Each section represents the odds of a particular possibility. This will always add to one, because that represents 100% of the possibilities.

If you want a different number of tosses, you just change the exponent for (p+q)^n. This is the binomal expansion of (p+q)^3, 3 being the number of tosses you have. P^3 + 3p^2q + 3pq^2 + q^3 (notice how this matches with the possibilities listed) To use math, you could do this: (p represents H, q represents T, and both are 1/2) There are 8 possibilities, and there are 3 ways to make 2T 1H, so it's 3/8.

Bayesian statistics khan academy