bridgesample: Calculates the marginal likelihood of a chain via bridge...

In BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

Calculates the marginal likelihood of a chain via bridge sampling

Description

Calculates the marginal likelihood of a chain via bridge sampling

Usage

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

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
posterior	posterior density function
...	arguments passed to bridge_sampler

Details

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

Author(s)

Tankred Ott

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")

# 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

BayesianTools documentation built on Feb. 16, 2023, 8:44 p.m.