- All Known Subinterfaces:
- DynamicBayesianNetwork
- All Known Implementing Classes:
- BayesNet, DynamicBayesNet
public interface BayesianNetwork
Artificial Intelligence A Modern Approach (3rd Edition): page 510.
Bayesian Networks are used to represent the dependencies among Random
Variables. They can represent essentially any full joint probability
distribution and in many cases can do so very concisely. A Bayesian network
is a directed graph in which each node is annotated with quantitative
probability information. The full specification is as follows:
1. Each node corresponds to a random variable, which may be discrete or
continuous.
2. A set of directed links or arrows connects pairs of nodes. If there is an
arrow from node X to node Y, X is said to be a parent of Y. The graph has no
directed cycles (and hence is a directed acyclic graph, or DAG.
3. Each node Xi has a conditional probability distribution
P(Xi | Parents(Xi)) that quantifies the effect of the
parents on the node.
The topology of the network - the set of nodes and links - specifies the
conditional independence relationships that hold in the domain.
A network with both discrete and continuous variables is called a hybrid
Bayesian network.
Note(1): "Bayesian Network" is the most common name used, but there
are many synonyms, including "belief network", "probabilistic network",
"causal network", and "knowledge map".
- Author:
- Ciaran O'Reilly