CBIOMES Bayesian Working Group

Bayesian CBIOMES: Statistical and software tools for integrating data and models

Contact: Greg Britten

Slack

Time-series observations of marine plankton populations contain important information on how organisms respond and interact with their environment. Robustly extracting this information is challenging, however, due to the many sources of variation often present in plankton time series, together with uncertainties in the structure and parameters of models we use to describe planktonic systems. 

Bayesian inference is a powerful mathematical framework to integrate mechanistic plankton models with observations while properly accounting for multiple sources of variation and uncertainty. On January 8-10, 2020, 28 CBIOMES researchers came together for a three-day workshop to learn and collaborate on Bayesian analyses, motivated by case-studies derived from ongoing CBIOMES projects. We learned the concepts behind Bayesian methods and modern software tools to implement hands-on analyses. 

At the subsequent Annual Meeting (in June 2020) we reviewed the motivation and material covered in the workshop and discussed the exciting science being done within CBIOMES via Bayesian methods, ending the session with a  group discussion to identify additional datasets, research projects, and collaborators that can utilize Bayesian methods and software going forward, and prospects for a second Bayesian CBIOMES workshop for 2021. 

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