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所在平台: Udemy |
课程主页: https://www.udemy.com/course/probabilistic-programming-with-stan/
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Coursera 课程《Probabilistic Programming with STAN》内容总结： 本课程专注于使用概率编程语言 STAN 进行统计推断，特别是在贝叶斯框架下。课程通过大量示例和小项目风格的实践，深入讲解了 STAN 的应用。 讲师在攻读应用数学研究生期间，深感缺乏编写和优化 STAN 代码的资源，为此历经艰辛才掌握相关技能。这段个人经历促使讲师创建此教程，旨在帮助更多人探索贝叶斯推断的丰富性。 课程重点涵盖以下 STAN 模型： * **多元回归模型 (Multi_variate Regression Models)** * **收敛性与模型调优 (Convergence and Model Tuning)** * **逻辑回归分析 (Logistic Regression Analysis)** * **二次预测模型 (Quadratic Predictive Models)** * **层级模型 (Hierarchical Models)** 讲师希望通过本课程，能够帮助学员更深入地理解贝叶斯思想，并在实践中更有效地运用贝叶斯方法。
In this course , the probabilistic programming for statistical inference , STAN , within Bayesian framework has been taught with many examples and mini-project styles.During my graduate studies in applied mathematics , I did not have the resources which teach me how to write the code and how to tune it , it took me such a long journey to teach myself , this then motivated me to create these tutorials for those who want to explore the richness of the Bayesian inference.This course , in details , explore the following models in STAN:- Multi_variate Regression Models - Convergence and Model Tuning- Logistic Regression Analysis - Quadratic Predictive Models - Hierarchical Models I hope this tutorial helps you to think more Bayesian and act more Bayesian.
