Calculates the marginal likelihood of a chain via bridge sampling

Calculates the marginal likelihood of a chain via bridge sampling

bridgesample(chain, nParams, lower = NULL, upper = NULL, ...)

Arguments

chain	a single mcmc chain with samples as rows and parameters and posterior density as columns.
nParams	number of parameters
lower	optional - lower bounds of the prior
upper	optional - upper bounds of the prior
...	arguments passed to bridge_sampler

Details

This function uses "bridge_sampler" from the package "bridgesampling".

See also

marginalLikelihood

Examples

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")
#> 
 Running DEzs-MCMC, chain  1 iteration 300 of 1002 . Current logp  -14.67601 -13.46839 -16.29187 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 600 of 1002 . Current logp  -14.8849 -14.08858 -15.73934 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 900 of 1002 . Current logp  -15.12236 -14.57723 -12.89714 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 1002 of 1002 . Current logp  -13.07464 -13.45026 -14.50715 . Please wait! 
#> runMCMC terminated after 0.980000000000018seconds

# 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)
#> Iteration: 1
#> Iteration: 2
#> Iteration: 3
#> Iteration: 4
#> Iteration: 5
#> Iteration: 6
#> Iteration: 7
#> Iteration: 8
#> Iteration: 9
#> Iteration: 10
bs_result
#> Bridge sampling estimate of the log marginal likelihood: -9.7342
#> Estimate obtained in 10 iteration(s) via method "normal".