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.
Imaging Inverse Problems and Bayesian Computation - Python tutorials to learn about (accelerated) sampling for uncertainty quantification and other advanced inferences
Generate text under lexical constraints.
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
Some interesting applications of Stochastic Processes using Jupyter Notebooks for descriptive and instructive illustrations.
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.
Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.
Approximate Bayesian Computation algorithm based on simulated annealing
Code the ICML 2024 paper: "EMC^2: Efficient MCMC Negative Sampling for Contrastive Learning with Global Convergence"
Final year undergraduate project focusing on inverse problems and Markov chain Monte Carlo methods.
Uncertainty Quantification for Physical and Biological Models
Running Monte Carlo - Markov Chain algorithm on synthesized spectral models made by CLOUDY to compare them with data from CECILIA survey
The repository houses the source code of paper
Gibbs samplers for inferring latent variables and learning the parameters of Bayesian hierarchical models.
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