Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict, combined with the law of ammunition consumption under different damage grades, a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed. The Bayesian inference model based on Gompertz distribution is constructed, and the system contribution degree is introduced to determine the weight of the multi-source information. In the case where the prior distribution is known and the distribution of the field data is unknown, the consistency test is performed on the prior information, and the consistency test problem is transformed into the goodness of the fit test problem. Then the Bayesian inference is solved by the Markov chain-Monte Carlo (MCMC) method, and the ammunition demand under different damage grades is gained. The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.

中文翻译：

---

基于 Gompertz 分布的弹药需求贝叶斯推理

针对新弹药消耗数据较少、需求难以预测的问题，结合不同损伤等级下弹药消耗规律，提出了一种基于Gompertz分布的弹药需求贝叶斯推理方法。构建基于Gompertz分布的贝叶斯推理模型，引入系统贡献度来确定多源信息的权重。在先验分布已知而现场数据分布未知的情况下，对先验信息进行一致性检验，将一致性检验问题转化为拟合优度问题。然后通过马尔可夫链-蒙特卡罗（MCMC）方法求解贝叶斯推理，获得不同伤害等级下的弹药需求。实例验证了该方法的准确性，解决了样本不足情况下弹药需求预测问题。