Bayesian Inference of Ammunition Consumption Based on Normal-Inverse Gamma Distribution - Haobang Liu; Xianming Shi; Xiaojuan Chen; Yuan Li; Mei Zhao; Yongchao Jiang (Discrete Dynamics in Nature and Society)
To address the problems of high cost of new ammunition experiment, few data of field test and low accuracy of consumption prediction, this article proposes a Bayesian estimation method of ammunition consumption based on normal-inverse gamma distribution, and estimates the hyperparameters in the prior distribution through the prior information from the consumption of ammunition under different damage degrees of point targets, based on the normal distribution phenomenon of ammunition consumption at each damage degree. It is to establish a Bayesian estimation model for ammunition consumption under different damage degrees according to field test data based on Bayesian formula and solve for its posterior distribution. The example proves that the estimation results of ammunition consumption for point target with different damage degrees based on this method is more scientific and reasonable according to various prior information.