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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.21673 |
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| _version_ | 1866915216603742208 |
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| author | Ramgraber, Maximilian Sharp, Daniel Provost, Mathieu Le Marzouk, Youssef |
| author_facet | Ramgraber, Maximilian Sharp, Daniel Provost, Mathieu Le Marzouk, Youssef |
| contents | Decision making under uncertainty is a cross-cutting challenge in science and engineering. Most approaches to this challenge employ probabilistic representations of uncertainty. In complicated systems accessible only via data or black-box models, however, these representations are rarely known. We discuss how to characterize and manipulate such representations using triangular transport maps, which approximate any complex probability distribution as a transformation of a simple, well-understood distribution. The particular structure of triangular transport guarantees many desirable mathematical and computational properties that translate well into solving practical problems. Triangular maps are actively used for density estimation, (conditional) generative modelling, Bayesian inference, data assimilation, optimal experimental design, and related tasks. While there is ample literature on the development and theory of triangular transport methods, this manuscript provides a detailed introduction for scientists interested in employing measure transport without assuming a formal mathematical background. We build intuition for the key foundations of triangular transport, discuss many aspects of its practical implementation, and outline the frontiers of this field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21673 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A friendly introduction to triangular transport Ramgraber, Maximilian Sharp, Daniel Provost, Mathieu Le Marzouk, Youssef Computation Atmospheric and Oceanic Physics Methodology Machine Learning 62-01, 62-02, 60-08, 65C05, 62F15, 65C20 Decision making under uncertainty is a cross-cutting challenge in science and engineering. Most approaches to this challenge employ probabilistic representations of uncertainty. In complicated systems accessible only via data or black-box models, however, these representations are rarely known. We discuss how to characterize and manipulate such representations using triangular transport maps, which approximate any complex probability distribution as a transformation of a simple, well-understood distribution. The particular structure of triangular transport guarantees many desirable mathematical and computational properties that translate well into solving practical problems. Triangular maps are actively used for density estimation, (conditional) generative modelling, Bayesian inference, data assimilation, optimal experimental design, and related tasks. While there is ample literature on the development and theory of triangular transport methods, this manuscript provides a detailed introduction for scientists interested in employing measure transport without assuming a formal mathematical background. We build intuition for the key foundations of triangular transport, discuss many aspects of its practical implementation, and outline the frontiers of this field. |
| title | A friendly introduction to triangular transport |
| topic | Computation Atmospheric and Oceanic Physics Methodology Machine Learning 62-01, 62-02, 60-08, 65C05, 62F15, 65C20 |
| url | https://arxiv.org/abs/2503.21673 |