➕Probabilistic-Artificial-Intelligence

Active Learning concerns methods to decide how to sample the most useful information in a specific domain; how can you select the best sample for an unknown model? Gathering data is very costly, we would like to create some principled manner to choose the best data point to humanly label in order to have the best model. In this setting, we are interested in the concept of usefulness of information. One of our main goals is to reduce uncertainty, thus, Entropy-based (mutual information) methods are often used....

Introduzione alle catene di Markov La proprietà di Markov Una sequenza di variabili aleatorie $X_{1}, X_{2}, X_{3}, \dots$ gode della proprietà di Markov se vale: $$ P(X_{n}| X_{n - 1}, X_{n - 2}, \dots, X_{1}) = P(X_{n}|X_{n-1}) $$ Ossia posso scordarmi tutta la storia precedente, mi interessa solamente lo stato precedente per sapere la probabilità attuale. Da un punto di vista filosofico/fisico, ha senso perché mi sta dicendo che posso predire lo stato successivo se ho una conoscenza (completa, (lo dico io completo, originariamente non esiste)) del presente....

The maximum entropy principle is one of the most important guiding motives in artificial artificial intelligence. Its roots emerge from a long tradition of probabilistic inference that goes back to Laplace and Occam’s Razor, i.e. the principle of parsimony. Let’s start with a simple example taken from Andreas Kraus’s Lecture notes in the ETH course of Probabilistic Artificial Intelligence: Consider a criminal trial with three suspects, A, B, and C. The collected evidence shows that suspect C can not have committed the crime, however it does not yield any information about sus- pects A and B....