Markov chain Monte Carlo (MCMC) is a class of algorithms for sampling from probability distributions. In the end, given a parametric model explaining the data, MCMC could also be used for parameter estimation, in which case I wonder what the different options for processing MCMC samples are. For monomodal distributions for instance, I can think of averaging the $n$ last samples of a run, averaging the last sample of different MCMC runs. What are the options for estimating posterior of parameters?
Are you looking for techniques, or might you be looking for packages, such as R's
As you note, you'd ignore the first N data points in each chain, which are called "burn-in". How large N is depends on how quickly your model converged and there are a whole host of methods that you can use to give an idea of convergence. (Some of these methods require multiple chains.) If you're not sure your run has become well-mixed (converged), any calculations you do with your results will be meaningless.
I would run M chains, drop the first N points from each chain, then pool the rest of the points together. Then do a density plot to see what kind of distribution you have. As you suggest, you can take the mean value. (Especially if you can see that the distribution is reasonably normal.)
I wouldn't average the last sample from different runs.