Metropolis within Gibbs sampling algorithm of a posterior distribution
Implements a Metropolis-within-Gibbs sampling algorithm for an arbitrary real-valued posterior density defined by the user
gibbs(logpost,start,m,scale,...)
logpost |
function defining the log posterior density |
start |
array with a single row that gives the starting value of the parameter vector |
m |
the number of iterations of the chain |
scale |
vector of scale parameters for the random walk Metropolis steps |
... |
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 |
vector of acceptance rates of the Metropolis steps of the algorithm |
Jim Albert
data=c(6,2,3,10) start=array(c(1,1),c(1,2)) m=1000 scale=c(2,2) s=gibbs(logctablepost,start,m,scale,data)
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