The Bayesian Integral Inference Method and Its Application in Accuracy Evaluation
Shifeng Zhang · Journal of Astronautics · 2009
The expression of prior distribution and prior information fusion are great importance in Bayesian theory's application.An approach about the integral inference of prior information is proposed based on one type weapon's complimentary test information in different conditions.The Dirichlet distribution is introduced as the prior distribution of importance factors in multi-source information and the inference model for importance factors are established by Bayesian networks.The posterior distribution can be obtained reasonably through the updated nodes by MCMC method.Hence,the problem of importance factors inference is settled.Simulation results show that this approach is able to fuse the prior distributions effectively and has a bright application prospect in accuracy evaluation.