贝叶斯推断（BAYESIAN INFERENCE）是一种应用于不确定性条件下的决策的统计方法。贝叶斯推断的显著特征是，为了得到一个统计结论能够利用先验信息和样本信息。
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By the time-discretization approach, this paper derives a stochastic difference equation from the geometric mean-reversion process and then a nonlinear regression model is established. By this way, the paper obtains the distribution and estimation for each parameter by Bayesian inference.
针对连续几何均值回复模型,通过时间离散化导出对应的随机差分方程,从而得到一个非线性回归模型,进而利用贝叶斯推断得到各个参数的估计及后验分布。
参考来源 - 几何均值回复模型的估计及应用—《湖南大学学报·自然科学版》—2010年第6期—龙源期刊网A Bayesian inference is based on posterior densities of the parameters, namely for the j-th parameterθ_j the conditional density .
一个贝叶斯推断是基于后验密度函数的参数的,也就是说对第j个参数θ_j的条件密度为。
参考来源 - 基于切片抽样MCMC方法的比较分析·2,447,543篇论文数据，部分数据来源于NoteExpress
文章提出了变异系数未知和已知情况下抗力标准值的贝叶斯推断公式，并予以验证。
In this paper, the bayes predict formula of the resistance characteristic value is put forward in the condition of the coefficient of variation is known or unknown, and an authentication is provided.
本文尝试通过实验进一步考察不完美信息下序贯决策的个体行为是否符合贝叶斯推断。
This paper attempts to study whether sequential individuals' behavior under imperfect information accords with the prediction of the Bayesian Model by doing economic experiments.
处于前后不同决策顺序位置的个体行动选择的分布没有显著差异，他们只能简单地进行一步贝叶斯推断。
The distribution of individuals' behavior in different decision-turns doesn't have significant differences, and they are only able to do the Bayesian calculation in one step simply.
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