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Neural Posterior Estimation with Differentiable Simulators
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Neural Posterior Estimation with Differentiable Simulators
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Simulation-Based Inference (SBI) is a promising Bayesian inference framework that alleviates the need for analytic likelihoods to estimate posterior distributions. Recent advances using neural density estimators in SBI algorithms have demonstrated the ability to achieve high-fidelity posteriors, at the expense of a large number of simulations ; which makes their application potentially very time-consuming when using complex physical simulations. In this work we focus on boosting the sample-efficiency of posterior density estimation using the gradients of the simulator. We present a new method to perform Neural Posterior Estimation (NPE) with a differentiable simulator. We demonstrate how gradient information helps constrain the shape of the posterior and improves sample-efficiency.
Forward citations
Cited by 3 Pith papers
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Joint inference of weak lensing convergence map and cosmology with diffusion models
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Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions
Score-augmented loss functions for neural likelihood surrogates in SBI deliver downstream inference performance equivalent to 10x more training data at under 1.1x training time cost on network and spatial process models.
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Keeping Score: Efficiency Improvements in Neural Likelihood Surrogate Training via Score-Augmented Loss Functions
Score-augmented loss functions with adaptive weighting improve neural likelihood surrogate quality in simulation-based inference at lower simulation cost for structured stochastic process models.
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