Welcome to STA 360, Fall 2022!
The readings/preparations for the class can be found at the very bottom of this file for reference. These are subject to change and will be updated as the course progresses. Please see the course webpage for the first few weeks of the course, videos, homeworks, and assignments.
Please find directory information below regarding each folder:
- exam-cover-page: Template of the coverage page for the examination
- TA-lab-material: This is material that Olivier Binette has prepared has potential solutions to lab exercises
- exercises: This is folders where exercises for exams can be found
- homeworks: All homework exercises can be found here, included source files in LateX and Rmd
- intro-to-webpage: This contrains a video regarding how to navigate the course webpage
- labs: This contains all lab material (and source) material for the semester
- lecturesModernBayes20: This contains all lecture material
- reading: This contains reading materials that have been posted to the repo
- syllabus: This contains the syllabus for the course
- videos: Past videos of the past courses that you can utilize only if you wish
- deprecated Information that should ignore completely
The course webpage is meant to be a place such that you don't have to interact with github unless you choose to. The goal is that nearly all course materials should be accessible from this one place.
This has summary information regarding the course times, labs, OH, hw deadlines, and exam dates. In addition, this has all the zoom codes you need as a reference guide.
An introductory video of the course webpage can be found here: https://github.com/resteorts/modern-bayes/blob/master/intro-to-webpage/intro-to-webpage.mp4?raw=true
The syllabus is a comprehensive place regarding expectations for STA 360.
Sakai will be used so you can see your grades throughout the semester. Homework submissions will be uploaded here on Sakai as well.
I suggest that you read all of Hoff and you will be expected to have read the readings that correspond with the notes before coming to class. There are also notes that I have written under readings/ that you may find helping as additional resources.
Before starting the course for the fall semester, I would highly recommend review the pre-req material for the course on the syllabus. Given the shortened semester, please make sure that you are 100 percent comfortable with the pre-req material before taking STA 360. If you have any questions regarding this, please reach out to me as soon as possible.
- Intro to R, Part I
- Intro to R, Part II
- Intro to R, Part III
- Intro to R, Part IV
- Intro to R, Part V
- Intro to R, Videos
- Reference Text: http://shop.oreilly.com/product/9780596809164.do (The R Cookbook, not to be confused with the one for graphics).
Please learn github if you're not already familiar as this is where the course resources are located. https://lab.github.com/). Homework releases and submissions will be done on Sakai.
If Sakai is problematic for you due to your location, please let me know in advance, so I can think of alternative options, such as uploads via github. If you are having internet issues, please let me know as well.
- Credible Intervals): Cred intervals are covered on pages 52 and 267 of Hoff.
- Read Ch 4.1--4.1 (Cred intervals) (http://www2.stat.duke.edu/~rcs46/books/bayes_manuscripts.pdf)
- Here is a brief intro from PSU on Multinomial sampling for a review: https://onlinecourses.science.psu.edu/stat504/node/59
- Statistical Inference, Review Ch 1 - 5.
- Statistical Inference Solutions
- Review Notes