Dynamic Belief Networks

(15th-September-2020)

• A dynamic belief network (DBN) is a belief network with regular repeated structure. It is like a (hidden) Markov model, but the states and the observations are represented in terms of features. Assume that time is discrete. If F is a feature, we write Ft as the random variable that represented the value of variable F at time t. A dynamic belief network makes the following assumptions:

• The set of features is the same at each time.

• For any time t>0, the parents of variable Ft are variables at time t or time t-1, such that the graph for any time is acyclic. The structure does not depend on the value of t (except t=0 is a special case).

• The conditional probability distribution of how each variable depends on its parents is the same for every time t>0.