Title: A brief introduction to Bayesian methods for parameter estimation, model tuning, and uncertainty quantification, 
with some applications to cloud microphysics 

Presenter: Marcus van Lier-Walqui 
 
Abstract: 
I'll give a brief introduction to Bayesian estimation and how and why it should be used for parameter esimation problem (e.g. 
model tuning) and uncertainty quantification (e.g. probabilistic ensemble forecasts) problems. I'll discuss popular methods for 
estimating a solution to Bayes theorem, such as Markov Chain Monte Carlo (MCMC) samplers and Ensembe Kalman Filters 
(EnKF), including strengths and weaknesses of each. Finally, I'll give a summary of some reseach I'm working on using 
Bayesian parameter estimation to cloud retrievals, cloud microphysics schemes, and climate model tuning.