Using RStan with Fayette Klaassen

This talk provides an introduction to what Bayesian statistics and RStan are and how you can get started using them. Fayette Klaassen will introduce how to write a Stan program and describe the workflow using Stan as well as go over some example Stan models. Finally, we'll go over some common troubleshooting tips and examples in practice.
Fayette Klaassen is a Postdoctoral Researcher in the Department of Global Health and Population at the Harvard T.H. Chan School of Public Health, where she works on Bayesian statistical models to predict COVID-19 infections.
She received her PhD in Methodology and Statistics from Utrecht University in 2020. In her doctoral research she focused on evaluating the efficiency of Bayesian hypothesis testing using inequality constraints. She is interested in developing and applying methods for evidence synthesis of replications or multiple data sources.
#bayesian #datascience #rstats #mcmc

[@ehoumanevans4783]

Thnk you for this presentation... my first full simple explanation on Bayesian modelling with Stan.

[@BandiBlue]

Amazing presentation!

[@chenjxing]

Is it possible to share the slides?

[@rusergroupatharvarddatasci7232]

Hi @chenjxing, the slides are available here: github.com/RUG-at-HDSI/meeting_materials/blob/main/2022_10_27/RStan/rstan-slides.pdf