This session will give an overview of why the Bayesian framework would be beneficial to actuaries and how it fits into the actuarial workflow. With the help of modern technologies (Stan and Python/R), it could be very efficient and practical to build models and evaluate model performance while remaining flexible with model specifications to account for actuarial judgements.
Learning Objectives:
Understand how Bayesian framework is used in actuarial workflow.
Build models for stochastic loss reserve estimates for Bayesian MCMC with Stan and Python/R
Evaluate and compare model performance on difference metrics
