Bayesian education

I am working on innovative approaches to introducing Bayesian statistics in the undergraduate statistics curriculum.

Grants:

  • National Science Foundation (NSF)
    • Project title: Advancing Bayesian Thinking in STEM.
    • Role: Principal Investigator (PIs: Dogucu (UC Irvine) and Herring (Duke University)).
    • Period: Dec. 2022 – Nov. 2024.
    • Amount: $300,000.00
  • Liberal Arts Collaborative for Digital Innovation (LACOL)
    • Project title: Bayesian Statistics.
    • Role: Principal Investigator.
    • Period: Oct. 2018 – June 2022.
    • Amount: $27,500.00
  • Liberal Arts Collaborative for Digital Innovation (LACOL)
    • Project title: Bayesian Inference with Python.
    • Role: Principal Investigator.
    • Period: Jan. 2020 – June 2020.
    • Amount: $3,000.00

Book Projects:

Albert, J. and Hu, J. (2019), “Probability and Bayesian Modeling”, Texts in Statistical Science, Chapman & Hall CRC Press. Text website

Teaching and learning materials:

  • A GitHub repo on Vassar’s Bayesian Statistics course material (lectures, labs, homework, cases studies etc.)
  • I maintain a GitHub repo on various resources for undergraduate Bayesian education – let me know if you have recommended material to be added to the repo!

Short courses and workshops:

  • “Bayes BATS”, an NSF-funded bootcamp for STEM educators to introduce Bayesian methods in their curriculum (co-presenter), July 2023 (link)
  • “Bayesian Thinking: Fundamentals, Computation, and Hierarchical Modeling”, ISI short course (co-presenter), January 2023
  • “Introducing Bayesian Statistical Analysis into Your Teaching”,  eCOTS 2022 pre-conference workshop (co-presenter), May 2022 (link)
  • “Introducing Bayesian Statistical Analysis into Your Teaching”,  USCOTS 2021 pre-conference workshop (co-presenter), June 2021 (link)
  • “Bayesian Thinking: Fundamentals, Computation, and Hierarchical Modeling”, JSM workshop (co-presenter), November 2020 (link)
  • “Introduction to Bayesian Inference in R”, Bureau of Labor Statistics short course, Washington D.C., October 2018

Presentations and panel discussions:

  • “Examples from Two Undergraduate Bayesian Courses”, Symposium on Data Science & Statistics (co-presenter), June 2021 (link)
  • “Bayesian Methods and the Statistics and Data Science Curriculum”, CAUSE & JSDSE webinar series (panelist), February 2021 (slides and recordings)
  • “Using CE Microdata in Undergraduate Statistics Courses”, 2019 Consumer Expenditure Surveys (CE) Microdata Users’ Workshop, Washington D.C., July 2019 (slide deck)
  • “Teaching an Undergraduate Bayesian Statistics Course”, Statistical Science Department Seminar, Duke University, NC, December 2017

Service:

  • Organizer and Discussant, “Introducing Bayesian Methods in Statistics and Data Science Curriculum” (topic-contributed), JSM 2023
  • Section Chair, Education Research and Practice Section, International Society for Bayesian Analysis (ISBA), 2023 – 2025
  • Organizer, “Recent developments in Bayesian education” (invited), ISBA 2022
  • Breakout Room Lead, “Teaching Bayesian Statistics”, Prepare to Teach 2021 (slide deck)
  • Organizer & Chair, “Thinking beyond the p-value: advancing Bayesian education for the undergraduates” (invited), Joint Statistical Meetings 2020 (summary)
  • Treasurer, Education Research and Practice Section, International Society for Bayesian Analysis (ISBA), 2017 – 2019
  • Organizer & Discussant, “Introducing Bayesian Statistics at Courses of Various Levels” (topic-contributed), Joint Statistical Meetings 2017

Peer-reviewed publications:

  • Kejzlar, V. and Hu, J. (forthcoming), Introducing variational inference in statistics and data science curriculum, The American Statistician. link to the published paper
  • Hu, J. and Dogucu, M. (2022), Content and computing outline of two undergraduate Bayesian courses: tools, examples, and recommendations, Stat SDSS 2021 Special Issue, 11(1), e452. Open Access
  • Dogucu, M. and Hu, J. (2022), The current state of undergraduate Bayesian education and recommendations for the future, The American Statistician, 76(4), 405-413. Open Access
  • Albert, J. and Hu, J. (2020), Bayesian computing in the undergraduate statistics curriculum, Journal of Statistics Education, 28(3), 236-247. Open Access
  • Johnson, A., Rundel, C., Hu, J., Ross, K. and Rossman, A. (2020), Teaching an undergraduate course in Bayesian statistics: a panel discussion, Journal of Statistics Education, 28(3), 251-261. Open Access
  • Hu, J. (2020), A Bayesian statistics course for undergraduates: Bayesian thinking, computing, and research, Journal of Statistics Education, 28(3), 229-235. Open Access

Work in progress: