Kernel Density Estimation in Python

Kernel density estimators for Julia

MCMC sample analysis, kernel densities, plotting, and GUI

⚡ Lightning fast density estimation in Julia ⚡

kramersmoyal: Kramers-Moyal coefficients for stochastic data of any dimension, to any desired order

Kernel Density Estimation and (re)sampling

PyBNesian is a Python package that implements Bayesian networks.

An R package to perform spatial analysis on networks.

Multivariate kernel density estimation

Kernel density estimation on a sphere

Kernel density estimation in Rust.

Random Forests for Density Estimation in Python

learning state-space targets in dynamical systems

fast kernel evaluation in high dimensions via hashing

A differentiable implementation of kernel density estimation in PyTorch.

Kernel density estimation via diffusion in 1d and 2d.

Equipping Diffusion Models with Differentiable Spatial Entropy for Low-Light Image Enhancement, CVPRW 2024. Best LPIPS in NTIRE chanllenge.

Predicting Bike Rental Demand using Linear and Non-linear Regression Models

An R package for kernel density estimation with parametric starts and asymmetric kernels.

B-Spline Density Estimation Library - nonparametric density estimation using B-Spline density estimator from univariate sample.

Codebase for "A Consistent and Differentiable Lp Canonical Calibration Error Estimator", published at NeurIPS 2022.

Kernel density estimation algorithm on GPU for point cloud visualization. ply/pcd/txt/bin are supported to import/export.

Introducción al Aprendizaje No Supervisado en Español

Assessment of individual specialization in the use of space by animals

A work-in-progress web application for ProADV. This project aims to revive the ProADV website, providing a user-friendly interface for the Python package that processes and analyzes acoustic Doppler velocimeter (ADV) data.

Kernel density integral transformation: feature preprocessing and univariate clustering (TMLR, 2023)

No description provided.

This repository offers a TensorFlow-based anomaly detection system for cell images using adversarial autoencoders, capable of identifying anomalies even in contaminated datasets. Check out our code, pretrained models, and papers for more details.

KEN: Unleash the power of large language models with the easiest and universal non-parametric pruning algorithm

Weighted Spatio-Temporal Kernel Density Estimation (wSTKDE)