Welcome to STA 360/602!

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:

🗂️ Folder descriptions

• 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

Tenative Course Schedule

The course webpage can be found at resteorts.github.io (see Teaching Tab). Revelant links should be on the webpage.

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).

Common distributions

Common Distriubtions

github

Please review github usage if you are rusty on this or unfamiliar.

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

Research Readings (to be updated)

• MCMC Convergence Diagnostics

  1. Roy (2020): https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-031219-041300
  2. Jones and Qin (2022): https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-040220-090158
  3. Aleshin Guendel and Steorts (2024): https://www.annualreviews.org/content/journals/10.1146/annurev-statistics-040522-114848

• Record Linkage/Entity Resolution

  1. Binette and Steorts (2022): https://www.science.org/doi/10.1126/sciadv.abi8021
  2. Steorts (2015): https://projecteuclid.org/journals/bayesian-analysis/volume-10/issue-4/Entity-Resolution-with-Empirically-Motivated-Priors/10.1214/15-BA965SI.full
  3. Marchant et al. (2022): https://arxiv.org/abs/1909.06039
  4. Sadinle (2017): https://www.tandfonline.com/doi/abs/10.1080/01621459.2016.1148612
  5. Enamorando et al. (2019): https://imai.fas.harvard.edu/research/linkage.html
  6. Kundinger et. al (2025): https://projecteuclid.org/journals/bayesian-analysis/advance-publication/Efficient-and-Scalable-Bipartite-Matching-with-Fast-Beta-Linkage-fabl/10.1214/24-BA1427.full
  7. Guha et at al. (2022): https://arxiv.org/pdf/2002.09119 (Causal Inference)

• Network Analysis

  1. Goldenberg (2009): https://arxiv.org/abs/0912.5410
  2. Fienberg (2012): https://www.stat.cmu.edu/~brian/780/hw01/Fienberg%20(2012)%20A%20Brief%20History%20of%20Statistical%20Models%20for%20Network%20Analysis%20and%20Open%20Challenges.pdf

• Variational Inference

  1. Blei et al. (2016): https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1285773

• Clustering

1. Dahl et. al (2022): https://www.tandfonline.com/doi/abs/10.1080/10618600.2022.2069779?casa_token=iZDqPfOhz_oAAAAA:RRaDVDExYSac6yOTu3C1vqblJR6Cw2UGKiKuVPbQMkHXmlg69fGLBBHmYAr0h5Lq8uNLUI9n5BlN

• Causal Inference

1. Reiter (2018): https://www.tandfonline.com/doi/pdf/10.1080/00029890.2000.12005156?casa_token=LsVoa7I-brAAAAAA:S5vvbIQyzsfnr-t1_ragnSQWdrQYv-RUl1ei0_KDn7dc25SSRXdWLgnR5dpXfJvtEB-6u2IO_DN0
2. Guha et. al (2022): https://projecteuclid.org/journals/bayesian-analysis/volume-17/issue-4/Bayesian-Causal-Inference-with-Bipartite-Record-Linkage/10.1214/21-BA1297.full
3. Wortman and Reiter (2018): https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7911?casa_token=iuJ2tPXV6VQAAAAA%3AjAcsrlbQSy2HztzCw4dfk1lzKYvM5gVPmoyeuoxIEoRoX2IulG8Y9EO5tX9vneFIs_-vdOIfbGtvPg