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For plate nodes in the DBN, the inference algorithm calculates the temporal beliefs, which are marginal posterior probability distributions as a function of time. Temporal beliefs are returned by Network.getNodeValue.

The dependency of the beliefs on time makes the temporal beliefs array larger. If a node has X outcomes and the slice count was set to Y, the matrix will have X * Y elements. The elements representing a single time slice are adjacent. Therefore, elements with indices [0..X-1] in the matrix are the beliefs for t=0, elements [X..2*X-1] are the beliefs for t=1 and so on.

Only the plate nodes have the temporal beliefs. Other temporal node types have normal beliefs (the number of elements in the belief array is equal to their outcome count).