I am using GA and MCMC for estimating the parameters of a transfer function. GA returns results that each parameter has one value when minimizing the error function, and MCMC returns the whole chain results for each parameter after the burnin, which gives a statistical distribution of the possible values for that parameter.
My first question is: What is the benefit of using MCMC over GA? Which one is better than other in what aspects?
My second question is: Do I get the mean value of each parameter from the distribution of the chain in order to get the fit for the transfer function? If so, do I do that with mean taking the mean value of the distribution? what if the distribution is skewed?
Thank you in advance
sorry I am new here, I didn't know how to tag this question.