Faithful graphical representations of local independence
(2024) 27th International Conference on Artificial Intelligence and Statistics, AISTATS 2024 238. p.2989-2997- Abstract
Graphical models use graphs to represent conditional independence structure in the distribution of a random vector. In stochastic processes, graphs may represent so-called local independence or conditional Granger causality. Under some regularity conditions, a local independence graph implies a set of independences using a graphical criterion known as δ-separation, or using its generalization, µ-separation. This is a stochastic process analogue of d-separation in DAGs. However, there may be more independences than implied by this graph and this is a violation of so-called faithfulness. We characterize faithfulness in local independence graphs and give a method to construct a faithful graph from any local independence model such that the... (More)
Graphical models use graphs to represent conditional independence structure in the distribution of a random vector. In stochastic processes, graphs may represent so-called local independence or conditional Granger causality. Under some regularity conditions, a local independence graph implies a set of independences using a graphical criterion known as δ-separation, or using its generalization, µ-separation. This is a stochastic process analogue of d-separation in DAGs. However, there may be more independences than implied by this graph and this is a violation of so-called faithfulness. We characterize faithfulness in local independence graphs and give a method to construct a faithful graph from any local independence model such that the output equals the true graph when Markov and faithfulness assumptions hold. We discuss various assumptions that are weaker than faithfulness, and we explore different structure learning algorithms and their properties under varying assumptions.
(Less)
- author
- Mogensen, Søren Wengel LU
- organization
- publishing date
- 2024
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of Machine Learning Research
- volume
- 238
- pages
- 9 pages
- conference name
- 27th International Conference on Artificial Intelligence and Statistics, AISTATS 2024
- conference location
- Valencia, Spain
- conference dates
- 2024-05-02 - 2024-05-04
- external identifiers
-
- scopus:85194134685
- language
- English
- LU publication?
- yes
- id
- bca4aad3-c791-43f7-b5f5-1e9330cc2c2d
- date added to LUP
- 2025-01-16 12:09:26
- date last changed
- 2025-04-04 14:48:42
@inproceedings{bca4aad3-c791-43f7-b5f5-1e9330cc2c2d,
abstract = {{<p>Graphical models use graphs to represent conditional independence structure in the distribution of a random vector. In stochastic processes, graphs may represent so-called local independence or conditional Granger causality. Under some regularity conditions, a local independence graph implies a set of independences using a graphical criterion known as δ-separation, or using its generalization, µ-separation. This is a stochastic process analogue of d-separation in DAGs. However, there may be more independences than implied by this graph and this is a violation of so-called faithfulness. We characterize faithfulness in local independence graphs and give a method to construct a faithful graph from any local independence model such that the output equals the true graph when Markov and faithfulness assumptions hold. We discuss various assumptions that are weaker than faithfulness, and we explore different structure learning algorithms and their properties under varying assumptions.</p>}},
author = {{Mogensen, Søren Wengel}},
booktitle = {{Proceedings of Machine Learning Research}},
language = {{eng}},
pages = {{2989--2997}},
title = {{Faithful graphical representations of local independence}},
volume = {{238}},
year = {{2024}},
}