class: left, bottom, inverse, title-slide # Bayesian Statistics ## Lecture 0: Course Introduction ### Yanfei Kang ### 2019/08/01 (updated: 2019-09-04) --- # General information - **Credits: ** 2 credits - **Lecturer: ** Yanfei Kang - **Language: ** Taught in Chinese. Materials are in English. - **Computer language: R** - **Reception hours: ** Questions concerned with this course can be asked during or after each lecture or via email. - **Lecture notes: ** available on https://yanfei.site/teaching/bs. --- # References 1. Bayesian computation with R (2nd Edition). Jim Albert. Springer. 2008. 2. [An Introduction to Bayesian Thinking](https://statswithr.github.io/book/index.html). Merlise Clyde *et.al*. 2019. 3. Bayesian data analysis (3rd Edition). Andrew Gelman *et.al*. 2014. --- # Examinations - Assignments (labs): 50% - Final exam: 50% --- # About assignments 1. Subject of your email: "BS2019Lab-N-Name-StudentID". 2. Email attachments: R script named as "BS2019Lab-N-Name-StudentID.R". 3. **Pls let me know who you are.** 4. **Pls write a decent email.** --- # Course contents - The Basics of Bayesian Statistics - Bayesian Thinking - Single-Parameter Models - Multi-Parameter Models - Introduction to Bayesian Computation - Markov Chain Monte Carlo Methods - Group Comparison and Hierarchical Models - Regression Models - Review --- class: inverse, center, middle # Let's have fun! ![](https://upload.wikimedia.org/wikipedia/commons/d/d4/Thomas_Bayes.gif)