How can posterior samples be used to make inferences in Bayesian statistics?
Assume that you can readily sample from the posterior distribution for a parameter theta, but you interested in making inferences for gamma = g(theta). Describe the process by which you can obtain posterior samples from the posterior distribution for gamma.
What is your favorite part about the class?
What is your least favorite part of the class?
What part of the class is most beneficial to your learning?
What could be changed/improved to facilitate your learning?