2023-07-03 15:10:13
NAUTILUS: boosting Bayesian importance nested sampling with deep learningA 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
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