Bayesian Network (thing) by Nebulon

A mathematical model for modelling and calculation on the causal influence between thing. Also called causal network or influence diagram.

Basicly, a Bayesesian Network is an oriented graph where the nodes are variables (representing some phenomena, e.g. I got drunk last night or "I have a hangover") and the edges represent causal influence (e.g. if i got drunk last knight, I'm more likely to have a hangover than if i didn't get drunk).

Each variable has a finite number of states. Variables which are not influenced by other variables has an a priori probability distribution between its states. Influenced variables have argumented probabilities, so that the probability that it is in each of its states is known, if the states of the variables that has causal influence on it are known.

Bayesian Networks are used in decision support systems and troubleshooters.

Bayes' Theorem	Thomas Bayes	All ravens are black	The Birth of Artificial Intelligence
Using gzip to do computational linguistics	information gain	Joint Probability Distribution	Hidden Markov Model
Distributed Checksum Clearinghouse	credibility theory	Neyman-Pearson Lemma	interesting number
Pascal's wager	k-fold cross validation	Andrei Markov	leave-one-out cross validation
prior restraint	Cognitive Artifact	Patrick Stewart	Right Understanding
Goodman's New Riddle of Induction	information resiliency	thoughtcreep	cross validation