Simulation from Bayesian normal sampling model
Gives a simulated sample from the joint posterior distribution of the mean and variance for a normal sampling prior with a noninformative or informative prior. The prior assumes mu and sigma2 are independent with mu assigned a normal prior with mean mu0 and variance tau2, and sigma2 is assigned a inverse gamma prior with parameters a and b.
normpostsim(data,prior=NULL,m=1000)
data |
vector of observations |
prior |
list with components mu, a vector with the prior mean and variance, and sigma2, a vector of the inverse gamma parameters |
m |
number of simulations desired |
mu |
vector of simulated draws of normal mean |
sigma2 |
vector of simulated draws of normal variance |
Jim Albert
data(darwin) s=normpostsim(darwin$difference)
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