Discrete Sparse. This is the standard type of distribution. All possible probabilities are available, but there is no requirement that all values be specified. During evaluation, uniform distributions are automatically supplied for unassessed probabilities.
Causally Independent. This type of distribution compresses the space of necessary probabilities by assuming that certain states of the parent nodes are mutually exclusive. See causal Independence for more information.
The type of the disribution used for a variable cannot easily be changed. Doing so usually results in losing any previous assessment.
Standard assessment is a table-based method. You can locate and edit a particular set of probabilities based upon the states of the parents of the variable.
Causally Independent, or CI assessment is a type of standard discrete assessment. It uses a special type of distribution based upon assumptions about conditional independence among the parents of a variable. Such distributions can be very convenient since they dramatically reduce the number of values that must be entered.
Asymmetric assessment is a tree-based method. You create sets of probabilities, organized as a decision tree, by making explicit distinctions between states of parents of the variable. The tree serves to reduce the number of values that must be entered, since entire branches can easily be given common values. You cannot perform asymmetric assessment on variables with causally independent distributions.
The mechanics of each of these assessment types have one goal in common: to minimize the number of distinct probabilities required to correctly specify the distribution.
