Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
Deep-learning for photoacoustic tomography from sparse data
2 Pith papers cite this work. Polarity classification is still indexing.
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Derives reception operators and adjoints for acoustic boundary measurements by establishing equivalence between integral formulations and full-waveform approximations.
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Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
Embedding selection mechanisms into generative simulators enables amortized Bayesian inference to produce debiased, well-calibrated posteriors without tractable likelihoods.
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A Full-waveform Approximation of Finite-Sized Acoustic Apertures: Forward and Adjoint Wavefields
Derives reception operators and adjoints for acoustic boundary measurements by establishing equivalence between integral formulations and full-waveform approximations.