A New Inference algorithm of Dynamic Uncertain Causality Graph based on
  Conditional Sampling Method for Complex Cases
  Dynamic Uncertain Causality Graph(DUCG) is a recently proposed model for
diagnoses of complex systems. It performs well for industry system such as
nuclear power plants, chemical system and spacecrafts. However, the variable
state combination explosion in some cases is still a problem that may result in
inefficiency or even disability in DUCG inference. In the situation of clinical
diagnoses, when a lot of intermediate causes are unknown while the downstream
results are known in a DUCG graph, the combination explosion may appear during
the inference computation. Monte Carlo sampling is a typical algorithm to solve
this problem. However, we are facing the case that the occurrence rate of the
case is very small, e.g. $10^{-20}$, which means a huge number of samplings are
needed. This paper proposes a new scheme based on conditional stochastic
simulation which obtains the final result from the expectation of the
conditional probability in sampling loops instead of counting the sampling
frequency, and thus overcomes the problem. As a result, the proposed algorithm
requires much less time than the DUCG recursive inference algorithm presented
earlier. Moreover, a simple analysis of convergence rate based on a designed
example is given to show the advantage of the proposed method. % In addition,
supports for logic gate, logic cycles, and parallelization, which exist in
DUCG, are also addressed in this paper. The new algorithm reduces the time
consumption a lot and performs 3 times faster than old one with 2.7% error
ratio in a practical graph for Viral Hepatitis B.
