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

Implementations of parallel tempering algorithms to augment samplers with tempering capabilities

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

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.

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

Generate text under lexical constraints.

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

Likelihood Inference Neural Network Accelerator

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

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

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.

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

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

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

Uncertainty Quantification for Physical and Biological Models

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

Approximate Bayesian Computation algorithm based on simulated annealing

Code used to constrain dark matter substructure in the solar neighborhood with Gaia eDR3 wide binaries.

The repository houses the source code of paper

A few proofs and examples related to ML/Prob and Optimisation