运用统计学理论，通过高斯回归过程建立了一种用于矿用红外甲烷传感器温度补偿的算法模型，研究了模型中各参数对数据拟合度和拟合误差的控制效果，在Matlab软件平台上验证了模型的合理性，并利用基于贝叶斯定理的训练算法对模型进行训练，构建了该温度补偿模型的数值仿真效果图。仿真结果表明，该模型误差小、精度高，能对不同温度下传感器的非线性波动进行良好的补偿。

With the statistical theory used, an algorithmic model of temperature compensation based on the Gaussian process regression is established for infrared methane sensors. The model parameters are studied to fit the data and to minimize the fitting error. In the Matlab software platform, the model is trained with the Bayesian algorithm and the numerical simulation of temperature compensation is performed. The simulation results show that the model has small error and high accuracy, and it can compensate the nonlinear fluctuation of sensor signal at different temperatures.