Nothing
means <- c(0, 1, 2)
sds <- c(1, 0.6, 3)
# log-likelihood
ll <- function (x) {
return(sum(dnorm(x, mean = means, sd = sds, log = TRUE)))
}
# lower and upper bounds for prior
lb <- rep(-10, 3)
ub <- rep(10, 3)
# create setup and run MCMC
setup <- createBayesianSetup(likelihood = ll,
lower = lb,
upper = ub)
out <- runMCMC(bayesianSetup = setup,
settings = list(iterations = 1000),
sampler = "DEzs")
# sample from MCMC output with "burn-in" of 25%
sample <- getSample(out$chain, start = 250, numSamples = 500)
# use bridge sampling to get marginal likelihood
bs_result <- bridgesample(chain = sample,
nParams = out$setup$numPars,
lower = lb,
upper = ub,
posterior = out$setup$posterior$density)
bs_result
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