| Interface | Description |
|---|---|
| BayesianNetwork |
Artificial Intelligence A Modern Approach (3rd Edition): page 510.
Bayesian Networks are used to represent the dependencies among Random Variables. |
| BayesInference |
General interface to be implemented by Bayesian Inference algorithms.
|
| ConditionalProbabilityDistribution |
A conditional probability distribution on a RandomVariable Xi:
P(Xi | Parents(Xi)) that quantifies the effect of the parents on Xi. |
| ConditionalProbabilityTable |
Artificial Intelligence A Modern Approach (3rd Edition): page 512.
A Conditional Probability Table, or CPT, can be used for representing conditional probabilities for discrete (finite) random variables. |
| ContinuousNode |
A node over a Random Variable that has a continuous domain.
|
| DiscreteNode |
A node over a Random Variable that has a countable domain (may be infinite).
|
| DynamicBayesianNetwork |
Artificial Intelligence A Modern Approach (3rd Edition): page 590.
A dynamic Bayesian network, or DBN, is a Bayesian network that represents a temporal probability model. |
| FiniteNode |
A node over a Random Variable that has a finite countable domain.
|
| Node |
Artificial Intelligence A Modern Approach (3rd Edition): page 511.
A node is annotated with quantitative probability information. |