Towards Reliable Simulation-based Inference
Scientific knowledge expands by observing the world, hypothesizing some theories about it, and testing them against collected data. When those theories take the form of statistical models, statistical analyses are involved in the process of testing and refining scientific hypotheses. In this thesis, we focus on statistical models that take the form of scientific simulators and provide background about how machine learning can be used for statistical analyses in this context. The first part of this thesis is about showing empirically that performing statistical analyses with machine learning involves a degree of approximation. Specifically, all statistical analyses involve a level of uncer...
| Authors | Arnaud Delaunoy |
|---|---|
| Primary category | stat.ML |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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