ABSTRACT
In this chapter we will cover the major classes of inference algorithms — exact and approximate — that have been developed over the past 20 years. As we will see, different algorithms are suited to different network structures and performance requirements. Networks that are simple chains merely require repeated application of Bayes’ Theorem. Inference in simple tree structures can be done using local computations and message passing between nodes. When pairs of nodes in the BN are connected by multiple paths the inference algorithms become more complex. For some networks, exact inference becomes computationally infeasible, in which case approximate inference algorithms must be used. In general, both exact and approximate inference are theoretically computationally complex (specifically, NP hard). In practice, the speed of inference depends on factors such as the structure of the network, including how highly connected it is, how many undirected loops there are and the locations of evidence and query nodes.
