基于误差修正的电线覆冰厚度贝叶斯模型及分析

通过对已有的电线覆冰厚度数据进行分析，建立了电线覆冰厚度时间序列模型，对覆冰厚度进行贝叶斯统计推断，然后运用基于Gibbs抽样的MCMC方法对推断模型进行参数估计，并对MCMC模型进行误差修正.在此基础上，运用WINBUGS软件，对Gibbs抽样得到的预测结果与极大似然估计的预测结果进行比较.比较分析结果表明：通过误差修正的贝叶斯推断方法在电线覆冰厚度预测上具有更高的准确性.

Bayesian Model of Ice Thickness and Its Analysis Based on Error Correction

A statistical inference from the existing ice thickness data was made. The time series model of ice thickness was established, and the statistical inference from ice thickness was made. Then, the parameter estimation of the inference model by MCMC was conducted on the basis of the Gibbs sampling. At last, error correction of the Markov Chain Monte Carlo(MCMC) model was carried out to solve the error increase problem when the sample data was not enough during the maximum likelihood estimation. A comparison between the Gibbs sampling results by WINBUGS software and the maximum likelihood estimation result was made. By analysis and comparison, it has been proved that the Bayesian method for error correction has a higher accuracy for the estimation of the ice thickness on the cable.