Bayesian statistics was a third year course at the universities I’ve attended. Keep in mind that Bayesian statistics uses the probability theory that is given in most probability and statistics modules, and requires distributional knowledge, something also taught by frequentist statistics. First year students will not have the knowledge of probability distributions or algebra to be able to find posteriors. And, of course, if you want to apply Bayesian statistics in practice you need some kind of programming skill, as most programs that do Bayesian analysis are not user friendly (a plague on winbugs house..).
Heres what Bayesian statistics courses tend to teach (in my experience)
-the basis of bayesian statistics (usually in light amounts)
-priors, and obtaining posteriors from them. Usually applied to simple examples with conjugate priors, a tough exam question might be to obtain the posterior of the mean mu and variance sigma of a normal distribution with normal prior and inverse gamma prior.
-Jeffrey’s prior (so they need to know what an information matrix is)
-an introduction to MCMC techniques, particularly gibbs sampling and random walk metropolis hastings.
Bayesian statistics was a third year course at the universities I’ve attended. Keep in mind that Bayesian statistics uses the probability theory that is given in most probability and statistics modules, and requires distributional knowledge, something also taught by frequentist statistics. First year students will not have the knowledge of probability distributions or algebra to be able to find posteriors. And, of course, if you want to apply Bayesian statistics in practice you need some kind of programming skill, as most programs that do Bayesian analysis are not user friendly (a plague on winbugs house..).
Heres what Bayesian statistics courses tend to teach (in my experience) -the basis of bayesian statistics (usually in light amounts) -priors, and obtaining posteriors from them. Usually applied to simple examples with conjugate priors, a tough exam question might be to obtain the posterior of the mean mu and variance sigma of a normal distribution with normal prior and inverse gamma prior. -Jeffrey’s prior (so they need to know what an information matrix is) -an introduction to MCMC techniques, particularly gibbs sampling and random walk metropolis hastings.