Chapter 6: Parametric Estimation

One big challenge when working with probability distributions is identifying the parameters in the distributions. For example, the exponential distribution has a parameter λ, and you can estimate it to get an idea of the mean and the variance of the distribution.

Parametric estimation is the process of estimating the underlying parameters that govern the distribution of a dataset. Parameters are not limited to those that define the shape of the distribution, but also the locations. For example, if you know that a dataset comes from a uniform distribution but you don't know the lower bound, a, and upper bound, b, of the distribution, you can also estimate the values of a and b as they are also considered legitimate parameters.

Parametric estimation is important because it gives you a good idea of the dataset with a handful of parameters, for example, the distributions and associated descriptive statistics. Although real-life examples...