Definitions for

**"Bayesian network"****Related Terms:**Stochastic model, Scatterplot, Scatter diagram, Histogram, Probability distribution, Variate, Statistical model, Var, Latent variable, Regression equation, Regression, Variability, Histogram, Markov chain, Control chart, Scatter plot, Logistic regression, Spatial autocorrelation, Random variable, Distribution, Correlation matrix, Regression analysis, Independent variable, Graph, Influence diagram, Confusion matrix, Deterministic, Indicator variable, Multicollinearity, Spurious correlation, Probability density function, Significant, Factor analysis, Extrapolation, Stochastic, Linear regression, Serial correlation, Monte carlo simulation, Kriging, Coefficient of determination, Error variance, Likelihood, Autocorrelation, Coefficient of correlation, Covariance, Dependent variable, Binomial distribution, Likelihood function, Multivariate, Variability

A technology used to model domain knowledge containing uncertainty - the core technology for building Hugin Knowledge Bases

a compact, expressive representation of uncertain relationships among parameters in a domain

a directed acyclic graph (DAG) rep resentation of the joint probability distribution for a set of random variables Horvitz et al

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.