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Found 6 papers, 5 papers with code
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence.
Balancing Simulation-based Inference for Conservative Posteriors
We show empirically that the balanced versions tend to produce conservative posterior approximations on a wide variety of benchmarks.
Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation
In this work, we introduce Balanced Neural Ratio Estimation (BNRE), a variation of the NRE algorithm designed to produce posterior approximations that tend to be more conservative, hence improving their reliability, while sharing the same Bayes optimal solution.
SAE: Sequential Anchored Ensembles
Anchored ensembles approximate the posterior by training an ensemble of neural networks on anchored losses designed for the optima to follow the Bayesian posterior.
A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms can produce computationally unfaithful posterior approximations.
Lightning-Fast Gravitational Wave Parameter Inference through Neural Amortization
Gravitational waves from compact binaries measured by the LIGO and Virgo detectors are routinely analyzed using Markov Chain Monte Carlo sampling algorithms.
