该学科为数学概率和概率建模的基本概念提供了全面的基础。 涵盖的主题包括随机实验和样本空间，概率公理和定理，离散和连续随机变量/分布（包括位置，展开和形状的度量），期望和生成函数，随机变量的独立性和依存性的度量（协方差和相关性） ，用于推导随机变量或它们的近似值的转换分布的方法（包括中心极限定理）。 在本课程中讨论的概率分布和模型在现实世界的应用程序中经常出现。 这些包括许多广泛使用的一维和二维（尤其是双变量正态）分布，以及基本概率模型，例如泊松过程和马尔可夫链。
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