estimatePDF

A Python library for Probability Density Function (PDF) estimation using Kernel Density, Histogram Density, and Dual Polynomial Regression.

Overview

estimatePDF estimatePDF is a Python package for probability density function (PDF) estimation and sampling. It provides computationally efficient, GPU-optimized implementations using TensorFlow along with custom polynomial regression methods designed to capture asymmetry in distributions.

Features

• Density Estimation

  • SciPy Gaussian KDE
  • TensorFlow-based KDE (for graph execution)
  • Histogram Density Estimation (HDE)

• Probability Density Functions

  • Gaussian PDF
  • Asymmetric Laplace PDF & sampling
  • M-Wright functions and variants
  • Inverse Transform Sampling

• Dual Polynomial Regression (DPR)

  • Piecewise polynomial PDF approximation
  • Gradient-based threshold detection
  • Fits multimodal or skewed PDFs

Installation

Install from PyPI:

pip install estimatePDF