Describe the steps of "metropolis hasting" drawing a sample from beta (1.5, 5) distribution with a normal jumping distribution with standard deviation of 2.

Got some wrror in accept.prob? How can Solve it?

mh.gamma <- function(n.sims, start, burnin, cand.sd, shape1, rate) {

theta.cur <- start

draws <- c()

theta.update <- function(theta.cur, shape, rate) {

theta.can <- rnorm(1, mean = theta.cur, sd = cand.sd)

accept.prob <- dbeta(theta.can, shape = shape, rate = rate)/dbeta(theta.cur,

shape = shape, rate = rate)

if (runif(1) <= accept.prob)

theta.can

else theta.cur

}

for (i in 1:n.sims) {

draws[i] <- theta.cur <- theta.update(theta.cur, shape = shape,

rate = rate)

}

return(draws[(burnin + 1):n.sims])

}

mh.draws<- mh.gamma (10000, start=1, burn=1000, cand.sd = 2, shape = 1.5, rate = 5)