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所在平台: Udemy |
课程主页: https://www.udemy.com/course/bayesian-intro/
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课程名称：数据科学的贝叶斯统计 课程概述：此课程教授统计学的基础知识，重点是掌握任何贝叶斯模型的基本组成部分——先验分布和似然函数，以及如何找到后验分布、可信区间和预测分布。在学习过程中，您将更熟悉概率论，并获得新的数据分析视角。本课程从基础开始，无需具备贝叶斯统计的经验，但要求学生具备基本的代数和算术知识。如果您希望运行可选的编码部分，需要使用R和RStudio或Python。 课程内容包括： - 5.5小时的视频讲座 - 使用R和Stan的互动演示（也包括Python代码） - 检查您理解的测验 - 带有解决方案的复习作业，以巩固所学知识 您将学习： - 概率的基本规则 - 贝叶斯定理，包括医疗测试和抛硬币的常见示例 - 贝叶斯模型不同组成部分的术语：先验分布、后验分布、似然函数和预测分布 - 共轭先验 - 可信区间和贝叶斯估计量 - 使用伯努利分布和二项分布来建模二元数据，以及Beta分布的先验 - 使用泊松分布来建模计数数据，以及伽马分布的先验 - 使用正态分布来建模连续数据，以及正态分布的先验 - 简单线性回归的介绍 本课程适合多种类型的学生： - 想要学习贝叶斯统计基础并理解先验、后验和可信区间等概念的任何人 - 希望刷新和扩展统计知识的数据科学和数据分析专业人士 - 社会科学、生物科学和物理科学的学术人士 该课程适合所有人，无论是初学者还是经验丰富的专业人士。无论您是刚开始数据科学的旅程，想要提升现有技能，还是对贝叶斯统计抱有兴趣，我的目标是让贝叶斯统计对所有人都能易于接触和理解。
This course teaches the foundational material of statistics covered in an introductory college course, with a focus on mastering the basic components of any Bayesian model - the prior distribution and the likelihood, and how to find a posterior distribution, credible intervals, and predictive distributions. Along the way, you'll become more comfortable with probability in general and gain a new perspective on how to analyze data!We start from scratch - no experience in Bayesian statistics is required. Students should have a strong grasp of basic algebra and arithmetic. R and RStudio, or Python, is required if you would like to run the optional coding sectionsThe course includes:5.5 hours of video lecturesInteractive demonstrations using R and Stan (Python code is included too!)Quizzes to check your understandingReview assignments with solutions to practice what you have learnedYou will learn:The basic rules of probabilityBayes' rule, including common examples with medical testing and flipping coinsThe terminology of different components of a Bayesian model: the prior distribution, posterior, likelihood, and predictive distributionConjugate priorsCredible intervals and Bayes estimatorsModeling binary data with the Bernoulli and Binomial Distribution, and the Beta distribution priorModeling count data with the Poisson Distribution, and the Gamma distribution priorModeling continuous data with the Normal Distribution, and the Normal distribution priorAn introduction to simple linear regressionThis course is ideal for many types of students:Anyone who wants to learn the foundations of Bayesian statistics and understand concepts like priors, posteriors and credible intervalsData science and data analytics professionals who would like to refresh and expand their statistics knowledgeAcademics in the social, biological, and physical sciencesThis course is ideal for anyone, from beginners to seasoned professionals. It doesn't matter if you're just starting your journey in data science, looking to upgrade your existing skills, or simply have an interest in Bayesian statistics. My goal is to make Bayesian statistics accessible and understandable for all.
