Utilities
A Kernel Density Estimator (KDE) is an object designed to allow evaluating Probability Density Functions (PDFs) from a set of sample data. It is useful to avoid considering as a reliable PDF one that is computed over a limited number of points.
This function sets the data over which the KDE will compute estimates.
Input:
- NewData: a list of floats representing counts over categories or bins.
This function estimates the PDF value for a specific point.
Input:
- Point: the point for which to estimate the probability.
This function returns a PDF over a set of points.
Input:
- Min: a float representing the minimum value of the interval in which to evaluate the distribution;
- Max: a float representing the maximum value of the interval in which to evaluate the distribution;
- Points: the number of points in which to discretize the space.
Output:
- Return value: a map of floats over floats. Keys represent the discretized interval points while values represent the corresponding probabilities.