STAT 9240 - Bayesian Inference
This course introduces Bayesian methods for statistical inferences. We will look over concepts of Bayesian theories and Bayesian coomputational tools. Mainly we will follow up Markov chain Monte Carlo (MCMC) sampling methods including independent samplings (rejection sampling, importance sampling) and dependent samplings (Metropolis-Hastings, Gibbs sampling, slice sampling, and sequential Monte Carlo methods). These MCMC methods can be utilized in the final project.
Grade Mode: Normal, Audit
Prerequisites: STAT 8620
Hours: Credit Hrs. Low: 3
Course Level: Graduate
Click here for the Schedule of Classes.
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