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.
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 to build 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 workshop's scientist-led team's extensive experience. 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 RStudio (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 distributions (normal, Poisson, etc.), would also be useful, though not essential.
- Each participant must bring a personal laptop, as all lab sessions will be done on it. Each participant will use RStudio Cloud to carry out tasks during 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 for those interested in, or who have heard about, Bayesian modeling in Environmental Health but have little theoretical or practical experience and want tools and know-how to get started in an approachable, friendly setting.
This two-day intensive workshop introduces Bayesian inference and modeling in 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 years of diverse, combined experience, the workshop will combine seminar lectures with hands-on computer sessions to put concepts into practice. The workshop will provide several examples using existing data and encourage discussions on starting new investigations based on attendees' research questions. The lectures and lab sessions will cover the principles of Bayesian inference, how to handle different data structures, available software options, 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, multidisciplinary 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. As Lead Instructor for the Columbia University SHARP Course on Bayesian Modeling for Environmental Health, Robbie brings this expertise to the forefront. Previously a Columbia University Earth Institute/Climate School Postdoctoral 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.