Databricks Exam Databricks-Certified-Professional-Data-Scientist Topic 2 Question 49 Discussion

In general, for a fixed set of data and underlying statistical model the method of maximum likelihood selects the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes the 'agreement' of the selected model with the observed data, and for discrete random variables it indeed maximizes the probability of the observed data under the resulting distribution. Maximum-likelihood estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution and many other problems. However in some complicated problems, difficulties do occur: in such problems, maximum-likelihood estimators are unsuitable or do not exist.