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Jan, 2024
利用嵌套MLMC进行具有难解似然函数的顺序神经后验估计
Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoods
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Xiliang Yang, Yifei Xiong, Zhijian He
TL;DR
本文提出了一种基于嵌套APT方法的序贯神经后验估计方法,利用无偏的多层次Monte Carlo估计器对偏差和方差进行校正,以近似具有中等维度的多模态复杂后验分布。
Abstract
sequential neural posterior estimation
(SNPE) techniques have been recently proposed for dealing with
simulation-based models
with intractable likelihoods. They are devoted to learning the posterior from adaptive
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