Bayesian Networks

Bayesian network

The Bayesian Network is an easy-to-understand graphical notation representing the conditional inter-dependence of variables within a system. This simple graphical formalism can leverage conditional probability distributions to describe relationships between variables in a system. How can Bayesian networks compute the probabilities of specific events given other facts? Humans also use this kind of reasoning to render decisions in uncertain environments.

A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.

Efficient algorithms can perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (e.g. speech signals or protein sequences) are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.

Source: Wikipedia, https://en.wikipedia.org/wiki/Bayesian_network
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