A transformer-based diffusion model learns the joint distribution of convergence maps and cosmology from log-normal weak lensing simulations and generates calibrated posterior samples matching MCMC results.
Neural posterior estimation with differentiable simulators
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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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Joint inference of weak lensing convergence map and cosmology with diffusion models
A transformer-based diffusion model learns the joint distribution of convergence maps and cosmology from log-normal weak lensing simulations and generates calibrated posterior samples matching MCMC results.
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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.