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

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

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"

Running Monte Carlo - Markov Chain algorithm on synthesized spectral models made by CLOUDY to compare them with data from CECILIA survey

Gibbs samplers for inferring latent variables and learning the parameters of Bayesian hierarchical models.

Code that coarse-grains the Quantum-enhanced Markov Chain Monte Carlo