Maximum Likelihood - MLE, parameter estimation

In general, for a fixed set of data and underlying probability model, the method of maximum likelihood selects values of the model parameters (eg. mean, variance) that produce the distribution most likely to have resulted in the observed data (i.e. the parameters that maximize the likelihood function). Maximum likelihood estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution and many other problems.

Generally assumes data are
- iid (independent and identically distributed)
- normal distribution

http://en.wikipedia.org/wiki/Maximum_likelihood