The basic tenets of Bayesian inference are introduced. This includes the construction of a prior and the use of the posterior distribution to perform inferences. Simulation is helpful in summarizing posterior distributions and Markov chain Monte Carlo algorithms are useful in simulating from posterior distributions of non-familiar forms.
KeywordsPosterior Distribution Bayesian Modeling Acceptance Rate Posterior Density Interval Estimate
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