calculates the Maxiumum APosteriori value (MAP)
an object of class BayesianOutput (mcmcSampler, smcSampler, or mcmcList)
optional values to be passed on the the getSample function
Currently, this function simply returns the parameter combination with the highest posterior in the chain. A more refined option would be to take the MCMC sample and do additional calculations, e.g. use an optimizer, a kerne delnsity estimator, or some other tool to search / interpolate around the best value in the chain
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