 Introduction  Mathematical Framework  Graphical Representation  Empirical Evidence  Criticisms  References

 Background  Mathematical Framework  Dynamics and Speed of Convergence  Dynamic Inefficiency  Golden Rules

In this paper, I provide a tutorial exposition on maximum likelihood estimation (MLE). The intended audience of this tutorial are researchers who practice mathematical modeling of cognition but are unfamiliar with the estimation method. Unlike least-squares estimation which is primarily a descriptive tool, MLE is a preferred method of parameter estimation in statistics and is an indispensable tool for many statistical modeling techniques, in particular in non-linear modeling with non-normal data. The purpose of this paper is to provide a good conceptual explanation of the method with illustrative examples so the reader can have a grasp of some of the basic principles

一、课程意义 二、课程学习的基本要求 三、物理定律 四、 微分方程 五、 场论初步、算符与常用函数

一、热传导与扩散方程定解问题 二、稳态方程的定解问题 三、物理系统可能涉及的其它几类条件

一、二阶线性偏微分方程理论 二、 δ函数

一、拉普拉斯方程定解问题分离变量求解 二、高维混合问题分离变量求解

一、边界条件齐次化 二、第三章总结

一、三维波动方程柯西问题的泊松公式 二、泊松公式的物理意义

电子科技大学：《数学物理方程与特殊函数 Mathematical Physics Equations with Special Function》课程教学资源（课件讲稿）第五章 积分变换 5.1 Fourier变换（1/2）