The Bayesian Modeling for Environmental Health Workshop is 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.
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
- Software options
- 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
AUDIENCE AND REQUIREMENTS
Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are three requirements to attend this training:
- 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.
ADDITIONAL INFORMATION
Capacity is limited. Paid registration is required to attend.
