Kernel density estimation is a process by which we can estimate the shape of a dataset. After we have computed the shape of a dataset, we can compute the probability in which an event will happen.
In this section, we're going to introduce the kernel density estimator. The kernel density estimator requires a kernel function, and we are going to discuss the requirements of the kernel function and how the normal distribution meets those requirements. Finally, we're going to compute the KDE of a set of values. So, kernel density estimation tries to estimate the shape of a dataset. All data has a shape - we could also refer to this as the density - and that shape is not always clear. Once we have estimated the shape of a dataset, we can compute the probability of a particular observation.
We require a kernel function, and in this section...
