Machine Learning Algorithms

MAP learning

When selecting the right hypothesis, a Bayesian approach is normally one of the best choices, because it takes into account all the factors and, as we'll see, even if it's based on conditional independence, such an approach works perfectly when some factors are partially dependent. However, its complexity (in terms of probabilities) can easily grow because all terms must always be taken into account. For example, a real coin is a very short cylinder, so, in tossing a coin, we should also consider the probability of even. Let's say, it's 0.001. It means that we have three possible outcomes: P(head) = P(tail) = (1.0 - 0.001) / 2.0 and P(even) = 0.001. The latter event is obviously unlikely, but in Bayesian learning it must be considered ...