pdf1d : Univariate Probability Density Function Library

Introduction

Provides several classes for representing and using univariate probability density functions (PDFs).

Each PDF comes as a builder-model-sampler triplet of objects.

e.g. The abstract PDF base classes

For instance, the univariate Gaussian PDF classes

A variety of types of simple PDF are implemented:

The models are used to calculate densities, cumulative probabilities, gradients, etc.

Kernel Estimates

The classes

Are kernel PDFs, ie representing a PDF by placing a kernel at every point in a sample. The builder includes a variety of algorithms taken from "Density Estimation" by B.W.Silverman (Pub. Chapman and Hall, 1986), including a recommended choice of kernel width and an implementation of the adaptive kernel estimate.

Examples of kernels available include

Mixture Models

Mixture models are represented using the pdf1d_mixture class. The pdf1d_mixture_builder uses the EM algorithm to estimate the parameters of the mixtures.

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