What is a Kernel Density Estimation?
The Kernel Density Estimation is a mathematic process of finding an estimate probability density function of a random variable. The estimation attempts to infer characteristics of a population, based on a finite data set. The data smoothing problem often is used in signal processing and data science, as it is a powerful way to estimate probability density. In short, the technique allows one to create a smooth curve given a set of random data. However, the estimation can also be used to generate points that only appear to have come from a specific sample set. This feature is particularly useful in project simulations and in object modeling.
How does a Kernel Density Estimation work?
Kernel Density Estimation and Machine Learning
The Kernel Density Estimation technique can be incorporated into machine learning applications. For example, as the estimation function has parameters to define the scope of the kernel, a neural network can begin to train itself to correct its estimations and produce more accurate results. As the estimation process repeats itself, the bandwidth and amplitude estimations are continuously updated to increase the accuracy of the estimated probability density curve.