Ying Nian Wu's UCLA Statistical Machine Learning Tutorial on generative modeling.
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Updated
Jan 7, 2023
Ying Nian Wu's UCLA Statistical Machine Learning Tutorial on generative modeling.
Joint Analysis and Imputation of generalized linear models and linear mixed models with missing values
Implementations of parallel tempering algorithms to augment samplers with tempering capabilities
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Likelihood Inference Neural Network Accelerator
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
Concept code for predicting precipitation using model fields (temperature, geopotential, wind velocity, etc.) as predictors for sub-areas across the British Isle.
Final year undergraduate project focusing on inverse problems and Markov chain Monte Carlo methods.
Implementation of Markov chain Monte Carlo sampling and the Metropolis-Hastings algorithm for multi-parameter Bayesian inference.
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.
The repository houses the source code of paper
A few proofs and examples related to ML/Prob and Optimisation
Cuadernos introductorios
Adaptive paralelle tempering for sampling multi-modal posteriors in NIMBLE.
Approximate Bayesian Computation algorithm based on simulated annealing
Inverse prompting LLMs for interpretability
Uncertainty Quantification for Physical and Biological Models
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