Optimization Monte Carlo reformulates stochastic simulator inference as gradient-based deterministic optimization for faster, accurate posterior estimation in high-dimensional or challenging settings.
In the experiments, we use a neural spline flow as density estimator (sbi.utils.get_nn_models.posterior_nn())
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Fast and Robust Simulation-Based Inference With Optimization Monte Carlo
Optimization Monte Carlo reformulates stochastic simulator inference as gradient-based deterministic optimization for faster, accurate posterior estimation in high-dimensional or challenging settings.