Extensions for Random to help you generate random content. IMPORTANT: this library was designed for use in games; no effort has been made to make these methods cryptographically secure.

BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.

Official implementation for the paper "Model-based Diffusion for Trajectory Optimization". Model-based diffusion (MBD) is a novel diffusion-based trajectory optimization framework that employs a dynamics model to run the reverse denoising process to generate high-quality trajectories.

MCMC sample analysis, kernel densities, plotting, and GUI

Python library for learning the graphical structure of Bayesian networks, parameter learning, inference and sampling methods.

Simulate a fisheries creel survey.

This is a repository for CS4ML. It is a general framework for active learning in regression problems. It approximates a target function arising from general types of data, rather than pointwise samples.

A class-leading water system implemented in Unity

OntoSample is a python package that offers classic sampling techniques for OWL ontologies/knowledge bases. Furthermore, we have tailored the classic sampling techniques to the setting of concept learning making use of learning problem.

Coding ML models, Sampling Methods, Feature Selection algorithms from scratch

A R package to perform uniform sampling in the environmental space

An ensemble of machine learning models for detecting fraudulent credit card transactions, utilizing advanced techniques for feature selection, data imbalance handling, and hyperparameter tuning.

Uni{corn|form} toolkit

A Python library for efficient feature ranking and selection on sparse data sets.

Parametric Voronoi generator with real time editing and SVG export

Sampling methods for data streams

Microsynthesis using quasirandom sampling and/or IPF

Methods for optimizing the spatial location of biodiversity-observation-networks

An open-source JAX-based statistical sampling toolkit 🧪

Design of experiments for Python