
Extended Distributed Learning Automata:A New Method for Solving Stochastic Graph Optimization Problems
In this paper, a new structure of cooperative learning automata socalle...
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Stochastic Simulation of Bayesian Belief Networks
This paper examines Bayesian belief network inference using simulation a...
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Simulation Approaches to General Probabilistic Inference on Belief Networks
A number of algorithms have been developed to solve probabilistic infere...
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A Quantum Observation Scheme Can Universally Identify Causalities from Correlations
It has long been recognized as a difficult problem to determine whether ...
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Sampling distribution for singleregression Granger causality estimators
We show for the first time that, under the null hypothesis of vanishing ...
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Sampling First Order Logical Particles
Approximate inference in dynamic systems is the problem of estimating th...
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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. 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 ratio in a practical graph for Viral Hepatitis B.
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