mcmc-sampling

🍓 Simulation-free, GPU-first generative modeling in PyTorch ⚡ Composable primitives for scalable, stable training of modern EBMs, diffusion, flow matching, and Schrödinger bridges.

Ying Nian Wu's UCLA Statistical Machine Learning Tutorial on generative modeling.

Implementations of parallel tempering algorithms to augment samplers with tempering capabilities

Numerics is a free and open-source library for .NET developed by USACE-RMC, providing a comprehensive set of methods and algorithms for numerical computations and statistical analysis.

Joint Analysis and Imputation of generalized linear models and linear mixed models with missing values

Imaging Inverse Problems and Bayesian Computation - Python tutorials to learn about (accelerated) sampling for uncertainty quantification and other advanced inferences

Bayesian Inference. Parallel implementations of DREAM, DE-MC and DRAM.

Generate text under lexical constraints.

Likelihood Inference Neural Network Accelerator

Gaussian Process Bayesian Toolkit with Monte Carlo Sampler Integration for Heavy Ion Collisions

Concept code for predicting precipitation using model fields (temperature, geopotential, wind velocity, etc.) as predictors for sub-areas across the British Isle.

A visual, hands-on introduction to Monte Carlo methods with clean simulations, intuitive explanations, and from-scratch implementations.

Contains different versions of an algorithm for MCMC sampling with linear CVs

Deep Learning methods (CNNs and VAEs) applied to the study of phase transitions (of the 2d Ising Model).

Some interesting applications of Stochastic Processes using Jupyter Notebooks for descriptive and instructive illustrations.

Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.

Code the ICML 2024 paper: "EMC^2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence"

Python package for retrieval of properties of exoplanets by model-fitting their transit light curves using MCMC with additional features such as detrending of light curves, GP regression, and continuous monitoring of the retrieval process.

Final year undergraduate project focusing on inverse problems and Markov chain Monte Carlo methods.

Uncertainty quantification, Bayesian inference, and scientific ML for physical/biological models