A New Inference algorithm of Dynamic Uncertain Causality Graph based on Conditional Sampling Method for Complex Cases
This work addresses inefficiency in DUCG inference for rare-event scenarios in domains like clinical diagnosis, though it is incremental as it builds on existing Monte Carlo sampling methods.
The paper tackles the variable state combination explosion problem in Dynamic Uncertain Causality Graph (DUCG) inference for complex cases like clinical diagnoses, where rare events (e.g., occurrence rates as low as 10^-20) cause inefficiency. It proposes a conditional sampling method that reduces time consumption, performing 3 times faster than the previous algorithm with a 2.7% error ratio in a practical Viral Hepatitis B graph.
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.