sampling-methods
There are 233 repositories under sampling-methods topic.
wave-harmonic/crest
A class-leading water system implemented in Unity
blackjax-devs/blackjax
BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.
huwb/volsample
Structured Volume Sampling - sample placement algorithm for real-time volume rendering with low aliasing, for camera-in-volume case.
erdogant/bnlearn
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
FunctionLab/selene
a framework for training sequence-level deep learning networks
wiseodd/MCMC
Collection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.
minaskar/zeus
⚡️ zeus: Lightning Fast MCMC ⚡️
guilgautier/DPPy
Python toolbox for sampling Determinantal Point Processes
LeCAR-Lab/model-based-diffusion
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.
WebSVG/voronoi
Parametric Voronoi generator with real time editing and SVG export
cmbant/getdist
MCMC sample analysis, kernel densities, plotting, and GUI
AndyShih12/paradigms
PyTorch implementation for "Parallel Sampling of Diffusion Models", NeurIPS 2023 Spotlight
LeCAR-Lab/CoVO-MPC
Official implementation for the paper "CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design" accepted by L4DC 2024. CoVO-MPC is an optimal sampling-based MPC algorithm.
jeremiecoullon/SGMCMCJax
Lightweight library of stochastic gradient MCMC algorithms written in JAX.
minaskar/pocomc
pocoMC: A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation
PolyChord/PolyChordLite
Public version of PolyChord: See polychord.co.uk for PolyChordPro
InhwanBae/NPSN
Official Code for "Non-Probability Sampling Network for Stochastic Human Trajectory Prediction (CVPR 2022)"
IBM/xgboost-smote-detect-fraud
Can we predict accurately on the skewed data? What are the sampling techniques that can be used. Which models/techniques can be used in this scenario? Find the answers in this code pattern!
Jokeren/GPA
GPU Performance Advisor
utk-team/utk
Uni{corn|form} toolkit
YashTrikannad/f110_rrt_star
RRT Star path planning for dynamic obstacle avoidance for the F110 Autonomous Car
juliusberner/sde_sampler
Improved sampling via learned diffusions (ICLR2024) and an optimal control perspective on diffusion-based generative modeling (TMLR2024)
probsys/fast-loaded-dice-roller
A near-optimal exact sampler for discrete probability distributions
AdaptiveParticles/LibAPR
Library for producing and processing on the Adaptive Particle Representation (APR).
Andrew-Helmer/pmj-cpp
"Progressive Multi-Jittered Sample Sequences" in C++
ChunyuanLI/pSGLD
AAAI & CVPR 2016: Preconditioned Stochastic Gradient Langevin Dynamics (pSGLD)
alan-turing-institute/PDSampler.jl
Piecewise Deterministic Sampler library (Bouncy particle sampler, Zig Zag sampler, ...)
relf/pyDOE3
Design of experiments for Python
danieleongari/awesome-design-of-experiments
Curated list of resources for the Design of Experiments (DOE)
JuliaDynamics/StreamSampling.jl
Sampling methods for data streams
sharmaroshan/SECOM-Detecting-Defected-Items
Anamoly Detection for Detecting Defected Manufactured Semi-Conductors, as in this case of Classification, the Defected Chips would be very less in comparison to perfect Chips so we have apply either Over-Sampling or Under-Sampling.
jimmyg1997/Python-Digital-Signal-Processing-Basics
📶 Python Scripts for the basics of Digital Signal Processing (DSP). Updating on a regular basis.
matteobreschi/bajes
Bayesian Jenaer software
Networks-Learning/counterfactual-llms
Code for "Counterfactual Token Generation in Large Language Models", Arxiv 2024.
outbrain/outrank
A Python library for efficient feature ranking and selection on sparse data sets.
virgesmith/humanleague
Microsynthesis using quasirandom sampling and/or IPF