Bayesian updating in causal probabilistic networks by local computations
speech signals or protein sequences) are called dynamic Bayesian networks.
Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
Then the situation can be modeled with a Bayesian network (shown to the right).
All three variables have two possible values, T (for true) and F (for false).
For example, the network can be used to find out updated knowledge of the state of a subset of variables when other variables (the evidence variables) are observed.Edges represent conditional dependencies; nodes that are not connected (there is no path from one of the variables to the other in the Bayesian network) represent variables that are conditionally independent of each other.Each node is associated with a probability function that takes, as input, a particular set of values for the node's parent variables, and gives (as output) the probability (or probability distribution, if applicable) of the variable represented by the node.Efficient algorithms exist that perform inference and learning in Bayesian networks.Bayesian networks that model sequences of variables (e.g.