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| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Udgivet: |
2026
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| Fag: | |
| Online adgang: | https://arxiv.org/abs/2601.06830 |
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Indholdsfortegnelse:
- A novel framework for density estimation under expectation constraints is proposed. The framework minimizes the Wasserstein distance between the estimated density and a prior, subject to the constraints that the expected value of a set of functions adopts or exceeds given values. The framework is generalized to include regularization inequalities to mitigate the artifacts in the target measure. An annealing-like algorithm is developed to address non-smooth constraints, with its effectiveness demonstrated through both synthetic and proof-of-concept real world examples in finance.