a Directed Acyclic Graph (DAG) where graph nodes represent variables
a directed acyclic graph in which each node
a directed acyclic graph that represents a factorization of a probability distribution
a directed graphical representation of probabilistic relationships that people find easy to understand and use, often because the relationships have a causal interpretation
a directed graph whose nodes represent random variables
a graphical model for probabilistic relationships among a set of variables
a graphical model for reasoning under uncertainty
a graphical model that encodes probabilistic relationships among variables of interest
a graph of relationships among variables in a data set
a high-level representation of a probability distribution over a set of variables that are used for building a model of the problem domain
a knowledge representation technique for use in expert system development
a modeling technique that provides a mathematically sound formula for representing and reasoning uncertainty, imprecision, or unpredictability in our knowledge
a model of cause and effect, consistent with the conditional independencies embedded within the overall probability density function
an annotated directed acyclic graph encoding a joint probability distribution
an efficient way to encode uncertain knowledge in a way which allows proper reasoning under that uncertainty
a probabilistic representation for uncertain relationships, which has proven to be useful for modeling real-world problems
a representation of the joint distribution over all the variables represented by nodes in the graph
a representation of the probabilistic relationships among distinctions about the world
A directed graph that that can be used to reason with probabilistic information.
(BN) A directed, acyclic graph in which nodes represent stochastic variables (either continuous variables or with discrete states) and the edges represent probabilistic influences (represented as conditional probabilities).
A bayesian network (or a belief network) is a directed acyclic graph which represents independencies embodied in a given joint probability distribution over a set of variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable or a hypothesis. They are not restricted to representing random variables; which forms the "Bayesian" aspect of a Bayesian network.