# marginalLikelihood-method: Compute the marginal likelihood of a converged model. In CNPBayes: Bayesian mixture models for copy number polymorphisms

## Description

The recommended function for fitting mixture models and evaluating convergence is through the ‘gibbs' function. This function will return a list of models ordered by the marginal likelihood. The marginal likelihood is computed using the Chib’s estimator (JASA, Volume 90 (435), 1995).

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```marginalLikelihood(model, params = mlParams()) ## S4 method for signature 'SingleBatchModel' marginalLikelihood(model, params = mlParams()) ## S4 method for signature 'SingleBatchPooled' marginalLikelihood(model, params = mlParams()) ## S4 method for signature 'MultiBatchModel' marginalLikelihood(model, params = mlParams()) ## S4 method for signature 'MultiBatchPooled' marginalLikelihood(model, params = mlParams()) ## S4 method for signature 'list' marginalLikelihood(model, params = mlParams(warnings = FALSE)) ```

## Arguments

 `model` An object of class `MarginalModel`, or a list of `MarginalModel`'s. Can also be an object of `BatchModel` or a list of such models. `params` A list containing parameters for marginalLikelihood computation. See `mlParams` for details.

## Value

A vector of the marginal likelihood of the model(s)

See `mlParams` for parameters related to computing the log marginal likelihood via Chib's estimator. See `gibbs` for fitting multiple mixture models and returning a list sorted by the marginal likelihood. See `marginal_lik` for the accessor.
 ```1 2 3 4 5 6``` ```## In practice, run a much longer burnin and increase the number of ## iterations to save after burnin mm <- SingleBatchModelExample mcmcParams(mm) <- McmcParams(iter=50, burnin=0, nStarts=0) mm <- posteriorSimulation(mm) marginalLikelihood(mm) ```