drorsh/modern-bayes

Modern Bayesian statistics, STA 360/602, Duke University, Department of Statistical Science

Welcome to STA 360, Fall 2020!!

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 note that I will not receive the new roster for the course until 5 days before the class starts or the TA's for the class, so patience would be greatly appreciated. I look forward to having everyone in class very much and getting to know you:)

Tenative Course Schedule

Course Webpage

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.

Cheat Sheet to Course

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.

Syllabus

The Google group is a place where starting on the first day of class, you can post a question regarding lecture, lab, or a homework assignment so that everyone can see it.

Google group

Sakai will be used so you can see your grades throughout the semester. Homework submissions will be uploaded here on Sakai as well.

Sakai

Suggested Readings

Hoff

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.

Required Material

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.

Introduction/Review to R

• 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).

github

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.

Other

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.

Other readings :

• 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

Other resources

• Data visualization
• Data wrangling
• Regression

230 Resources

• Statistical Inference, Review Ch 1 - 5.
• Statistical Inference Solutions
• Review Notes

Practice Problems for Exams

Practice Problems for Exams

Homeworks

Homeworks will be updated on github during the course of the semester. Due dates are posted on the assignment. Students should upload their solutions to each homework assignment on Sakai. Please plan to submit your homework early and often to make sure it's uploaded before the deadline. Please note that homeworks are on the course webpage now with due dates in case you wish to start working on these or taking a look at the upcoming semester.

• Homework 1 (Due Friday August 21, 5 PM EDT)
• Homework 2 (Due Friday August 28, 5 PM EDT)
• Homework 3 (Due Friday September 4, 5 PM EDT)
• Homework 4 (Due Friday September 11, 5 PM EDT)
• Homework 5(TBD)
• Homework 6(TBD)
• Homework 7(TBD)

Exam 1

• Exam 1: Thursday, September 17th.
• Lab03 (or with prior approval from instructor): 8:00 AM - 9:15 AM EDT, September 17th
• Lab01 and Lab02: 1:45 - 3:00 PM EDT, September 17th

Exam 2

Date and Format TBD

Final Exam

• Final Exam: November 22, 2:00 - 5:00 PM EDT.