rejection-sampling

Mathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.

Speculative decoding orchestrator for LLM serving: draft-model ensemble with rejection sampling, KV-cache-aware admission control and self-tuning draft lengths

Minimal server-rendered dashboard framework with zero dependencies - layouts, widgets, and escape-safe rendering in plain Node.js

Julia package for fast flexible adaptive rejection sampling for log-concave univariate densities.

Some methods to sampling data points from a given distribution.

Approximate UniFrac via Weighted MinHash 🦀

Code library for the DRMD framework from 'DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift'.

Adaptive Rejection Sampling for Python

Monte Carlo methods with TensorFlow

A C++ and WPF implementation of Computer-Graphics-Principles And Practice by JOHN F. HUGHES, ANDRIES VAN DAM, MORGAN MCGUIRE, DAVID F. SKLAR, JAMES D. FOLEY, STEVEN K. FEINER and KURT AKELEY

We implement the Schnorr proof system in assembler via the Jasmin toolchain, and prove the security (proof-of-knowledge and zero-knowledge) and the absence of leakage through timing side-channels of that implementation in EasyCrypt.

Monte is a set of Monte Carlo methods in Python. The package is written to be flexible, clear to understand and encompass variety of Monte Carlo methods.

Some implementations of partial rejection samplers

Implementation of inference techniques like Variable Elimination and Rejection Sampling for Bayesian Networks. (CSL302 IIT Ropar)

Acceptance and rejection method

MATLAB code to generate pairwise, uncorrelated encounters

Monte Carlo Penalty Selection for graphical lasso

End-to-End Python framework reproducing Dolphin et al.'s (2026) grounded event extraction (from SEC 8-K filings) method. Extracts taxonomy-validated, quote-grounded event tags via LLMs, then proves economic significance through variance-standardized abnormal returns (SAR), Kruskal-Wallis tests, and bootstrap variance decomposition over CRSP data.

Kriging with inequality constraints for shape-restricted function estimation

Implementation for bayesian network