Probability Density Function Estimation Library
Project description
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
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