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所在平台: Udemy |
课程主页: https://www.udemy.com/course/bayesian-modelling-with-regression-from-a-to-z-with-r/
课程评论：没有评论
课程名称：贝叶斯回归建模（从A到Z）与R 课程概述： 在这门课程中，我们将深入探索贝叶斯方法在回归分析中的应用。正如L. Frank Baum所言，“没有任何小偷能够夺走知识，因此知识是最值得获取的财富。”在我攻读应用数学研究生学位时，面对众多贝叶斯理论和复杂的数学方程，我感到无比困惑，特别是在尝试首次应用贝叶斯方法进行项目时，更是无从下手。为了解决这一难题，我开设了这门课程，旨在帮助学生、研究人员和实践者从头到尾地掌握贝叶斯回归，以便顺利进行概率推断。 在课程中，我会将贝叶斯和非贝叶斯框架下的同一模型进行比较，使学员能够直观感受到两种方法的区别及其推断结果的不同。另外，对于对预测建模感兴趣的学员，我会提供关于真实数据的模型比较、模型选择、交叉验证，以及如何可视化建模不确定性的讲解。 课程还特别设置了一节课，以复习非贝叶斯框架下的单调和加性模型，作为正式课程的热身。感谢加入这段精彩的旅程，我希望我们能共同形成一个社区，分享见解、提出问题，并在此基础上继续学习贝叶斯知识。 让我们开始这段旅程吧！
"No thief, however skillful, can rob one of knowledge, and that is why knowledge is the best and safest treasure to acquire."― L. Frank Baum, The Lost Princess of OzWhen I was doing my graduate studies in Applied Mathematics , I was overwhelmed with the number of the books in Bayesian with many theories and wonderful mathematical equations , but I was completely paralyzed when I started my first project trying to apply Bayesian methods. I did not know where should I start and how to interpret any parameters which I made an inference about , there was not enough sources to walk me through from A to Z.I hope this lectures fills in that gap and acts as a bridge that help you as student , researcher or practitioner who wants to apply Bayesian methods in regression in order to successfully make the probabilistic inference.At each step , I would run the same model both in Bayesian and non-Bayesian framework , in order to enable you to see the difference between two different approaches and see how you need to interpret the difference.Also , for those who are interested in predictive modelling , I have included lectures on real data for model comparison , model selection , cross validation and ultimately methods to visualize the uncertainty in your modelling.However , before we start in complete Bayesian , I devoted one lecture to remind you of what we have seen in monotone and additive models in Non_Bayesian and , I look at it as a warm up before we start the course together.I`d like to thank you for joining me for this wonderful journey and I hope we an all form a community starting from here , in order to share our insights , questions and continue to work together to lean more about Bayesian.Let`s begin the journey...
