Počet záznamů: 1
Nonparametric estimations and the diffeological Fisher metric
- 1.0544050 - MÚ 2022 RIV CH eng C - Konferenční příspěvek (zahraniční konf.)
Le, Hong-Van - Tuzhilin, A. A.
Nonparametric estimations and the diffeological Fisher metric.
Geometric Structures of Statistical Physics, Information Geometry, and Learning. Cham: Springer, 2021 - (Barbaresco, F.; Nielsen, F.), s. 120-138. Springer Proceedings in Mathematics & Statistics, 361. ISBN 978-3-030-77956-6. ISSN 2194-1009.
[Statistical Physics, Information Geometry and Inference for Learning (SPIGL'20). Les Houches (FR), 27.07.2020-31.07.2020]
Institucionální podpora: RVO:67985840
Klíčová slova: Fisher metric * functorial language * probabilistic morphisms
Obor OECD: Pure mathematics
https://doi.org/10.1007/978-3-030-77957-3_7
In this paper, first, we survey the concept of diffeological Fisher metric and its naturality, using functorial language of probabilistic morphisms, and slightly extending Lê’s theory in [Le2020] to include weakly Ck-diffeological statistical models. Then we introduce the resulting notions of the diffeological Fisher distance, the diffeological Hausdorff–Jeffrey measure and explain their role in classical and Bayesian nonparametric estimation problems in statistics.
Trvalý link: http://hdl.handle.net/11104/0321109
Počet záznamů: 1