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Proper Scoring Rules - Estimation and Forecast Evaluation

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RCLW04 - Early Career Pioneers in Uncertainty Quantification and AI for Science

Proper scoring rules have been a subject of growing interest in recent years, not only as tools for evaluation of probabilistic forecasts but also as methods for estimating probability distributions. In this talk, we review the mathematical foundations of proper scoring rules including general characterization results and important families of scoring rules and discuss their role in statistics and machine learning for estimation and forecast evaluation. Based on joint work with Prof. Johanna Ziegel, ETH Zurich.

This talk is part of the Isaac Newton Institute Seminar Series series.

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