** Also study the first three review sheets!

I. Introduction to Bayesian Inference
 A. Rationale behind Bayesian Methods vs. Classical Methods
   1. Treating Parameters as Random Variables
   2. Specification of Prior Distribution
 B. Finding the Posterior
   1. Writing the Likelihood and Prior
   2. Joint pdf of data and parameter
   3. Marginal distribution of data
   4. Posterior as the conditional distribution of parameter given the data
   5. Prior and Likelihood => Posterior
   6. Proportionality and role of Normalizing Constant
 C. Conjugate Priors and Examples
   1. Definition of Conjugate prior for a sampling model
   2. The Binomial likelihood & Beta prior Bayesian Model
   3. The Exponential/Gamma likelihood & Exponential/Gamma prior Bayesian Model
   4. Relationship of posterior to sufficient statistics
   5. The Normal likelihood & Normal prior Bayesian Model
 D. Bayesian point estimators
   1. Posterior mean as a combination of the sample mean/MLE and prior mean
   2. Posterior median, posterior mode (don't worry about how to obtain these)
   3. Point estimator for a function of the parameter
   4. Finding bias, variance, and MSE of Bayesian estimators
 E. Effect of Prior on Posterior Inference
   1. Choosing prior parameters to reflect prior knowledge
   2. Noninformative priors
 F. Posterior Credible Intervals
   1. Formal Definition of Credible Interval
   2. Differences in Interpretation of Credible Interval vs. Confidence Interval
   3. Finding credible intervals with normal posterior
   4. Knowing we can use R functions (qbeta, qgamma, etc.) to find credible intervals in other cases