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Found 2 papers, 2 papers with code
Parallelized Acquisition for Active Learning using Monte Carlo Sampling
Bayesian inference remains one of the most important tool-kits for any scientist, but increasingly expensive likelihood functions are required for ever-more complex experiments, raising the cost of generating a Monte Carlo sample of the posterior.
Fast and robust Bayesian Inference using Gaussian Processes with GPry
GPry does not need any pre-training, special hardware such as GPUs, and is intended as a drop-in replacement for traditional Monte Carlo methods for Bayesian inference.
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