| Interface | Description |
|---|---|
| CategoricalDistribution |
Artificial Intelligence A Modern Approach (3rd Edition): page 487.
A probability distribution for discrete random variables with a finite set of values. |
| CategoricalDistribution.Iterator |
Interface to be implemented by an object/algorithm that wishes to iterate
over the possible assignments for the random variables comprising this
categorical distribution.
|
| Factor |
Artificial Intelligence A Modern Approach (3rd Edition): page 524.
Each factor is a matrix indexed by its argument variables. |
| Factor.Iterator |
Interface to be implemented by an object/algorithm that wishes to iterate
over the possible assignments for the random variables comprising this
Factor.
|
| FiniteProbabilityModel |
Artificial Intelligence A Modern Approach (3rd Edition): page 484.
A probability model on a discrete, countable set of worlds. |
| ProbabilityDensity |
Artificial Intelligence A Modern Approach (3rd Edition): page 487.
A probability distribution for continuous random variables. |
| ProbabilityDistribution |
Artificial Intelligence A Modern Approach (3rd Edition): page 487.
A probability distribution is a function that assigns probabilities to events (sets of possible worlds). Note: This definition is slightly different than that given in AIMA3e pg. |
| ProbabilityMass |
Artificial Intelligence A Modern Approach (3rd Edition): page 487.
A probability distribution for discrete random variables. |
| ProbabilityModel |
Artificial Intelligence A Modern Approach (3rd Edition): page 484.
A fully specified probability model associates a numerical probability P(ω) with each possible world. |
| RandomVariable |
Artificial Intelligence A Modern Approach (3rd Edition): page 486.
Variables in probability theory are called random variables and their names begin with an uppercase letter. |