# bridgesample: Calculates the marginal likelihood of a chain via bridge... In BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

 bridgesample R Documentation

## 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

`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.