indepmetrop {LearnBayes} | R Documentation |
Simulates iterates of an independence Metropolis chain with a normal proposal density for an arbitrary real-valued posterior density defined by the user
indepmetrop(logpost,proposal,start,m,...)
logpost |
function defining the log posterior density |
proposal |
a list containing mu, an estimated mean and var, an estimated variance-covariance matrix, of the normal proposal density |
start |
vector containing the starting value of the parameter |
m |
the number of iterations of the chain |
... |
data that is used in the function logpost |
par |
a matrix of simulated values where each row corresponds to a value of the vector parameter |
accept |
the acceptance rate of the algorithm |
Jim Albert
data=c(6,2,3,10)
proposal=list(mu=array(c(2.3,-.1),c(2,1)),var=diag(c(1,1)))
start=array(c(0,0),c(1,2))
m=1000
fit=indepmetrop(logctablepost,proposal,start,m,data)