Teaching

Teaching is a major motivation in my academic career; both personally, and because working at the interface of statistics and environmental science requires strong knowledge transfer between the disciplines. To this end, I've developed a number of workshops and course materials to teach stats tools to environmental students and researchers. Code and workshop materials are linked below. They will be updated and improved on over time.

Workshop and Course Materials


Bayesian modeling of ecological systems using the 'Stan' software package (01/16-30/2020)

Developed with Paul Mattern as part of the Independent Activities Period at MIT. The course develops a Bayesian analysis of a marine ecosystem model via simulation, model fitting, prior specification, predictive analyses, and uncertainty quantification. The model analysis is done in Python. Fitting the model is done in Stan.

Materials: https://github.com/gregbritten/BayesianEcosystems_IAP


Bayesian Modelling of Dynamic Marine Systems (01/08-10/2020)

Developed with Paul Mattern as part of the Collaboration on Computational Biogeochemical Modelling of Marine Ecosystems (CBIOMES). The workshop materials apply the MCMC package Stan to a series of case studies. All codes to call Stan are written in Julia, R, and Python, inclusively. Lecture materials include a brief introduction to Bayesian inference and the science behind the case studies.

Materials: https://github.com/jpmattern/bayesian_cbiomes


Introduction to Spatial-Temporal Statistics (04/27/2017)

Developed with Yara Mohajerani as part of the Data Science Initiative at UCI. Topics cover introductory concepts in applied time series and spatial analysis using a few key R packages. The notes also include instructions linking Python to R via the package rPy2, allowing Python users to access the many statistics packages in R.

Materials: https://github.com/gregbritten/Introduction-Spatial-Temporal-Stats