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所在平台: Udemy |
课程主页: https://www.udemy.com/course/probability-and-random-variables-with-python-applications/
课程评论：没有评论
**课程概览：概率与随机变量** 本课程深入讲解概率论和随机变量的基础知识。课程首先会介绍概率论的基本概念，如试验、样本空间和事件。随后，将深入探讨重复试验、排列、组合以及乘法法则等概念。 在掌握了基础概率概念后，课程将引入随机变量。通过具体示例，详细讲解概率质量函数。此外，还将重点介绍几种重要的概率分布，包括伯努利分布、均匀分布和泊松分布等。课程还会涵盖累积分布函数、联合分布以及联合分布函数的计算方法。 课程将区分离散随机变量和连续随机变量。离散随机变量的取值是有限的或可数的，而连续随机变量的取值则构成实数轴上的一个区间或多个不重叠区间的并集。为了处理连续随机变量，课程会介绍新的数学工具，并展示其与离散随机变量理论的类比性。 在学习完离散随机变量后，课程将重点转向连续随机变量，并介绍概率密度函数。与离散随机变量部分类似，课程将继续讲解概率密度函数在各种概率分布中的应用，如同离散随机变量部分一样，涵盖广泛的主题。
In this course probability and random variables are thought. First, the basic concepts from of probability concepts such as experiment, sample space, events are explained. Then, repeated trials, permutations, combinations, and multiplication rule, etc. are explained. Following basic topics of probability, random variables are introduced. Probability mass functions are explained by examples. Well known probability distributions, such as Bernoulli, uniform, poisson etc, are examplified. Cumulative distribution function, joint distribution, and calculation of joint distribution functions are taught. A random variable is continuous if possible values comprise either a single interval on the number line or a union of disjoint intervals.Discrete random variables can take only a countable number of possible values. On the other hand, a continuous random variable has a range in the form of an interval or a union of non-overlapping intervals on the real line (possibly the whole real line). Thus, we need to develop new tools to deal with continuous random variables. The good news is that the theory of continuous random variables is completely analogous to the theory of discrete random variables. After studying the discrete random variables, we focus on continuos random variables, and introduce probability density function. Then, we cover the same set of topics as it is done for discrete random variables.
