layout: true --- class: inverse, center, middle background-image: url(../figs/titlepage16-9.png) background-size: cover <br> <br> # Bayesian Statistics and Computing ## Lecture 0: Course Introduction <img src="../figs/slides.png" width="150px"/> #### *Yanfei Kang | BSC | 2021 Spring* --- # General information - **Credits: ** 2 credits - **Lecturer: ** Yanfei Kang. For now, you can know me better via my personal webpage: http://yanfei.site. - **Language: ** Taught in Chinese. Materials are in English. - **Computer language: R** - **Reception hours: ** Questions concerned with this course can be asked via email: firstname.lastname@example.org. - **Lecture notes: ** available on on https://yanfei.site/teaching/bsc2021. --- # References 1. [Advanced statistical computing](https://bookdown.org/rdpeng/advstatcomp/). Roger Peng. 2018. 2. [R programming for data science](https://bookdown.org/rdpeng/rprogdatascience/). Roger Peng. Leanpub. 2019. --- # Further reading Introduction to scientific programming and simulation using R. Owen Jones, Robert Maillardet, Andrew Robinson. 2nd Edition. CRC press. 2014. ISBN: 9781466569997. --- # Unit objectives ![The process of statistical modeling.](./sc.png) --- # Unit objectives 1. Learn R programming for data science. 2. Master optimization tools. 3. Understand Bayesian methods and computing. 4. Learn about advanced topics with applications, such as computational linear algebra techniques including eigenanalysis and singular value decomposition, and their applications. --- # Examinations - Assignments (labs): `\(50\%\)` - Final exam: `\(50\%\)` --- # About assignments 1. Please use course center for assignment submission. 2. Only one single pdf or html file is acceptable. I recommend you write your assignments using [Rmarkdown](https://rmarkdown.rstudio.com/), a productive notebook interface to weave together narrative text and code to produce elegantly formatted output. 3. Use meaningful file names.