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

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

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

Likelihood Inference Neural Network Accelerator

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

Generate text under lexical constraints.

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

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