Category: 6. Uncertain Knowledge R

Bayesian belief network is key computer technology for dealing with probabilistic events and to solve a problem which has uncertainty. We can define a Bayesian network as: “A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph.” It is also called a Bayes… Continue reading Bayesian Belief Network in AI

Bayes’ theorem: Bayes’ theorem is also known as Bayes’ rule, Bayes’ law, or Bayesian reasoning, which determines the probability of an event with uncertain knowledge. In probability theory, it relates the conditional probability and marginal probabilities of two random events. Bayes’ theorem was named after the British mathematician Thomas Bayes. The Bayesian inference is an application of Bayes’ theorem, which… Continue reading Bayes theorem in Artificial intelligence

Uncertainty: Till now, we have learned knowledge representation using first-order logic and propositional logic with certainty, which means we were sure about the predicates. With this knowledge representation, we might write A→B, which means if A is true then B is true, but consider a situation where we are not sure about whether A is… Continue reading Probabilistic reasoning in AI