Bayesian Network

Definition of Bayesian Network

A Bayesian network, also called a belief network, is a probabilistic graphical model that represents a set of random variables and their conditional dependencies. Variables are represented as nodes in the graph, and edges connecting nodes represent conditional dependencies between variables. Conditional probabilities are specified for the edges in the graph. A Bayesian network can be used to infer the likelihoods of different outcomes given the data.

What is Bayesian Network used for?

A Bayesian Network is a probabilistic graphical model used to represent and reason with uncertain knowledge. It can be used to build predictive models of complex systems, where the relationships between variables are not known in advance. A Bayesian Network encodes the conditional probabilities of events and variables (represented by nodes) using directed arcs. The arrows indicate which nodes are dependent upon one another, allowing for easy representation of conditional probability distributions. Using these structures, a Bayesian Network can calculate the probability of future events based on past data. For example, it could be used to predict the probability of an individual having a certain disease based on their symptoms or to forecast stock prices given macroeconomic indicators. As well as being useful for predictive modeling, Bayesian Networks can be used for causal inference and decision making under uncertainty. By considering relationships between variables in its structure, it can make use of prior knowledge about the system in order to produce more accurate predictions than traditional machine learning algorithms that do not take into account such relationships. Additionally, with its graphical representation, Bayesian Networks allow people to visually understand complex data sets and draw insights from them quickly and easily.