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Found 3 papers, 1 papers with code
Leveraging Nested MLMC for Sequential Neural Posterior Estimation with Intractable Likelihoods
Although atomic APT was proposed to solve this by discretizing the normalizing constant, it remains challenging to analyze the convergence of learning.
An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation
Sequential neural posterior estimation (SNPE) techniques have been recently proposed for dealing with simulation-based models with intractable likelihoods.
Conditional Density Estimations from Privacy-Protected Data
Many modern statistical analysis and machine learning applications require training models on sensitive user data.
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