Bayesian Modeling for Environmental Health
Master Bayesian modeling in Environmental Health through interactive seminars and hands-on sessions in this two-day workshop. Gain practical insights into concepts, techniques, and data analysis methods.
August 14-15, 2025
Modules/Weeks
Weekly Effort
Discipline
School
Format
Cost
Course Description
A two-day intensive course of seminars and hands-on analytical sessions to provide an approachable and practical overview of concepts, techniques, and data analysis methods used in Bayesian modeling with applications in Environmental Health.
- Learn the principles of Bayesian modeling in Environmental Health through interactive seminar lectures, gaining a solid understanding of the underlying concepts.
- Engage in hands-on computer sessions to apply theoretical knowledge practically. Explore existing data examples and initiate discussions on launching investigations based on participants' research questions.
- Acquire practical know-how on dealing with different data structures and explore various software options available for Bayesian modeling, empowering learners to navigate diverse analytical tools.
- Benefit from the extensive experience of the workshop's scientist-led team. Receive a comprehensive overview of Bayesian inference principles, different analysis types, and insights into current and future research in Environmental Health.
To contact support for this course, please email [email protected].
Course Prerequisites
- Basic familiarity with R and R studio (how to download R/R studio, and how to install a package) is recommended to get the most out of the workshop.
- Familiarity with spatial and temporal data structures, as well as exponential family distribution types (normal, Poisson etc.) would also be useful, though not essential.
- Each participant is required to bring a personal laptop as all lab sessions will be done on your personal laptop. Each participant will be using RStudio Cloud to carry out tasks while attending the Workshop. Instructions for the basics of RStudio Cloud(link is external and opens in a new window).
What You Will Learn
This training is needed for those who are interested or who have heard about Bayesian modeling and work in Environmental Health, but who have little theoretical or practical experience of it and would like some tools and know-how to get started in an approachable and friendly setting.
This two-day intensive workshop introduces the ideas of Bayesian inference and modeling in the context of Environmental Health, designed to be as approachable and friendly as possible while still providing technical and practical know-how. Led by a team of scientists with many years of diverse combined experience, the workshop will integrate seminar lectures with hands-on computer sessions to put concepts into practice. Several examples will be given using existing data, and conversations on starting new investigations with attendees' research questions will also be encouraged. The lectures and lab sessions will give an overview of the principles of Bayesian inference, as well as how to deal with different data structures, the various software options available, different types of analyses, and current and future research.
By the end of the workshop, participants will be familiar with the following topics:
- Principles of Bayesian inference
- Practicalities of Bayesian inference
- Priors and hyperpriors
- Different data structures (spatial, point, continuous, categorical)
- Advantages and drawbacks of Bayesian approaches
- Temporal modeling
- Spatial modeling
- Spatiotemporal modeling
- Hierarchical modeling
- Forecasting
- Software options
- Examples of use
- Examples of current and future research
Instructors
Robbie Parks is an environmental epidemiologist with extensive experience in large-scale, multi-disciplinary quantitative research, focusing on climate-related exposures, public health, and equity. Holding a tenure-track Assistant Professor position at Columbia University's Mailman School of Public Health, Robbie is also an NIH NIEHS K99/R00 Fellow. Serving as the Lead Instructor for the Columbia University SHARP Course on Bayesian Modeling for Environmental Health, Robbie brings expertise to the forefront. Previously a Columbia University Earth Institute/Climate School Post-doctoral Fellow from 2019 to 2022, collaborating with Prof. Marianthi-Anna Kioumourtzoglou, Robbie earned their PhD at the School of Public Health at Imperial College London in 2019 under the guidance of Profs. Majid Ezzati and Ralf Toumi. They graduated with a BA/MA (Oxon) in Physics from the Keble College, University of Oxford. Proudly embodying the roles of a first-gen academic and an Agents of Change in Environmental Justice Senior Fellow, Robbie is committed to making meaningful contributions to their field.