  In using the Bayesian network (BN) to construct the complex multistate
system's reliability model as described in Part I, the memory storage
requirements of the node probability table (NPT) will exceed the random access
memory (RAM) of the computer. However, the proposed inference algorithm of Part
I is not suitable for the dependent system. This Part II proposes a novel
method for BN reliability modeling and analysis to apply the compression idea
to the complex multistate dependent system. In this Part II, the dependent
nodes and their parent nodes are equivalent to a block, based on which the
multistate joint probability inference algorithm is proposed to calculate the
joint probability distribution of a block's all nodes. Then, based on the
proposed multistate compression algorithm of Part I, the dependent multistate
inference algorithm is proposed for the complex multistate dependent system.
The use and accuracy of the proposed algorithms are demonstrated in case 1.
Finally, the proposed algorithms are applied to the reliability modeling and
analysis of the satellite attitude control system. The results show that both
Part I and Part II's proposed algorithms make the reliability modeling and
analysis of the complex multistate system feasible.
