|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/bayesian-computational-analyses-with-r/
课程评论：没有评论
课程名称：R语言中的贝叶斯计算分析 概述：R语言中的贝叶斯计算分析是一门关于贝叶斯建模使用与实现的入门课程。贝叶斯方法是“频率主义”方法的替代方案，后者仅通过数据样本推断总体参数的可能性。与此不同，贝叶斯方法利用似然函数和观察数据样本（“先验”）来估计最可能的值和分布（“后验”）。这门课程适合希望了解贝叶斯概念的初学者和中级学员，同时也为贝叶斯实践者提供了实用的知识。课程包含大量使用R脚本和软件的实践示例，同时也对贝叶斯概念进行了深入的解释。课程材料包括所有教材、软件、R脚本、幻灯片、练习及其解答。学员在学习之前，如果具备一些基本的推断统计与概率论知识会更有帮助，尽管没有R语言的经验也可以参与。 课程内容概述： - 第一部分：12个视频课时的R语言和R脚本基础介绍，帮助初学者熟悉RStudio和R命令。 - 第二部分：介绍贝叶斯定理，展示离散先验、贝塔先验和贝塔后验的示例。 - 第三部分：解释和演示单参数模型的贝叶斯估计，例如估计均值或标准差但不同时估计这两个值。 - 第四部分：讲解“共轭混合模型”，即先验和后验函数形式相似的单参数模型，通过混合来同时测试多个竞争理论。 - 第五部分：涉及多参数贝叶斯模型的建模，其中同时估计多个后验变量的值，如均值和标准差。 - 第六部分：扩展贝叶斯讨论，通过估计积分来估算概率。 - 第七部分：介绍贝叶斯方法在拒绝抽样和重要抽样中的应用。 - 第八部分：比较和验证贝叶斯模型的示例和应用。 本课程为学员提供了理论与实践的结合，是对贝叶斯分析感兴趣者的理想选择。
Bayesian Computational Analyses with R is an introductory course on the use and implementation of Bayesian modeling using R software. The Bayesian approach is an alternative to the "frequentist" approach where one simply takes a sample of data and makes inferences about the likely parameters of the population. In contrast, the Bayesian approach uses both likelihood functions and a sample of observed data (the 'prior') to estimate the most likely values and distributions for the estimated population parameters (the 'posterior'). The course is useful to anyone who wishes to learn about Bayesian concepts and is suited to both novice and intermediate Bayesian students and Bayesian practitioners. It is both a practical, "hands-on" course with many examples using R scripts and software, and is conceptual, as the course explains the Bayesian concepts. All materials, software, R scripts, slides, exercises and solutions are included with the course materials. It is helpful to have some grounding in basic inferential statistics and probability theory. No experience with R is necessary, although it is also helpful.The course begins with an introductory section (12 video lessons) on using R and R 'scripting.' The introductory section is intended to introduce RStudio and R commands so that even a novice R user will be comfortable using R. Section 2 introduces the Bayesian Rule, with examples of both discrete and beta priors, predictive priors, and beta posteriors in Bayesian estimation. Section 3 explains and demonstrates the use of Bayesian estimation for single parameter models, for example, when one wishes to estimate the most likely value of a mean OR of a standard deviation (but not both). Section 4 explains and demonstrates the use of "conjugate mixtures." These are single-parameter models where the functional form of the prior and post are similar (for example, both normally distributed). But 'mixtures' imply there may be more than one component for the prior or posterior density functions. Mixtures enable the simultaneous test of competing, alternative theories as to which is more likely. Section 5 deals with multi-parameter Bayesian models where one is estimating the likelihood of more than one posterior variable value, for example, both mean AND standard deviation. Section 6 extends the Bayesian discussion by examining the estimation of integrals to estimate a probability. Section 7 covers the application the Bayesian approach to rejection and importance sampling and Section 8 looks at examples of comparing and validating Bayesian models.
