Chapter 5. Statistical Inference

In the previous chapter, we came across numerous tools that gave first insights of exploratory evidence into the distribution of datasets through visual techniques as well as quantitative methods. The next step is the translation of these exploratory results to confirmatory ones and the topics of the current chapter pursue this goal. In the Discrete distributions and Continuous distributions sections of Chapter 1, Data Characteristics, we came across many important families of probability distribution. In practical scenarios, we have data on hand and the goal is to infer about the unknown parameters of the probability distributions.

This chapter focuses on one method of inference for the parameters using the maximum likelihood estimator (MLE). Another way of approaching this problem is by fitting a probability distribution for the data. The MLE is a point estimate of the unknown parameter that needs to be supplemented with a range of possible values...