🚀 NAUTILUS: boosting Bayesian importance nested sampling with deep learning A novel approach to boost the efficiency of the importance nested sampling (INS) technique for Bayesian posterior and evidence estimation using deep learning. Nautilus - это проект от MIT на Python для оценки байесовской апостериорной вероятности. Nautilus обладает высокой точностью,  по сравнению с традиционными методами оценки МСМС и Nested Sampling. Пример: pip install nautilus-sampler import corner import numpy as np from nautilus import Prior, Sampler from scipy.stats import multivariate_normal prior = Prior() for key in 'abc': prior.add_parameter(key) def likelihood(param_dict): x = [param_dict[key] for key in 'abc'] return multivariate_normal.logpdf(x, mean=[0.4, 0.5, 0.6], cov=0.01) sampler = Sampler(prior, likelihood) sampler.run(verbose=True) points, log_w, log_l = sampler.posterior() corner.corner(points, weights=np.exp(log_w), labels='abc') 🖥 Github: https://github.com/johannesulf/nautilus ⭐️ Docs: https://nautilus-sampler.readthedocs.io/ 📕 Paper: https://arxiv.org/abs/2306.16923v1 ai_machinelearning_big_data