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Julia Linhart

2 accepted papers

2026

Diffusion posterior sampling for simulation-based inference in tall data settings

ICML 2026poster

Identifying the parameters of a non-linear model that best explain observed data is a core task across scientific fields. When such models rely on complex simulators, evaluating the likelihood is typically intractable, making traditional inference methods such as MCMC inapplicable. Simulation-based …

Cited by 0SourcecodeScholar
2023

L-C2ST: Local Diagnostics for Posterior Approximations in Simulation-Based Inference

NeurIPS 2023poster

Many recent works in simulation-based inference (SBI) rely on deep generative models to approximate complex, high-dimensional posterior distributions. However, evaluating whether or not these approximations can be trusted remains a challenge. Most approaches evaluate the posterior estimator only in…