Sequential Monte Carlo in python

Samplin' Safari is a research tool to visualize and interactively inspect high-dimensional (quasi) Monte Carlo samplers.

Lightweight and easy generation of quasi-Monte Carlo sequences with a ton of different methods on one API for easy parameter exploration in scientific machine learning (SciML)

Quasi-Monte Carlo point generators, automatic transformations, and adaptive stopping criteria

generation of Sobol low-discrepancy sequence (LDS) for the Julia language

Quasi-Monte-Carlo numerical computation of multivariate normal probabilities

Robust estimations from distribution structures: III. Non-asymptotic

(t, m, s)-nets generator / Генератор (t, m, s)-сетей

This is the official repository for the ICML 2026 paper "Neural Low-Discrepancy Sequences"

A simple quasi-random number generator implemented in C++ for generating low discrepancy sequences in any number of dimensions.

R package with quasi-Monte Carlo methods to estimate mixed models commonly used for random effect structures from pedigrees.

The aim of this project is to compare different pricing methods for an Asian option. Comparisons will be made in terms of MSE, CPU time and (empirical) variance of estimators.

Python package and open direction-number data for high-dimensional Sobol’ sequences, including the 50,000-dimensional tkrg-a-ap5 set for quasi-Monte Carlo simulation.

Some randomization methods for Randomized Quasi Monte Carlo e.g. scrambling, shift

A fast, automated program for calculating moment magnitude using full P, SV, and SH energy components with rapid spectral fitting for improved accuracy and efficiency.

Fast construction of Gaussian Process Regression models supporting gradient information.

construction of digital nets via Niederreiter algorithm

Powerful and flexible stochastic differential equation (quasi) Monte-Carlo simulation library written in Rust with Python bindings

Code of the paper The Robust Randomized Quasi Monte Carlo method, applications to integrating singular functions by E. Gobet M. Lerasle and D. Métivier

Python derivatives analytics with Monte Carlo and Sobol pricing, Black-Scholes validation, variance reduction, Greeks, implied volatility, and Streamlit.