- Abstract:
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We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAG models that allow for certain hidden variables. We present a first method for this problem using a greedy score-based search algorithm. We also prove some necessary and some sufficient conditions for distributional equivalence of BAPs which are used in an algorithmic approach to compute (nearly) equivalent model structures. This allows us to in...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Version:
- Publisher's version
- Files:
-
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(pdf, 677.9kb)
-
- Publisher copy:
- 10.1214/17-EJS1372
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- Publisher:
- Institute of Mathematical Statistics Publisher's website
- Journal:
- Electronic Journal of Statistics Journal website
- Volume:
- 11
- Issue:
- 2
- Pages:
- 5342–5374
- Publication date:
- 2017-12-28
- Acceptance date:
- 2017-11-17
- DOI:
- ISSN:
-
1935-7524
- Pubs id:
-
pubs:746200
- URN:
-
uri:83bd7ba3-9ecc-4165-8608-10c2eb669fe6
- UUID:
-
uuid:83bd7ba3-9ecc-4165-8608-10c2eb669fe6
- Local pid:
- pubs:746200
- Copyright holder:
- Nowzohour et al.
- Copyright date:
- 2017
- Notes:
- This article is licences under the Creative Commons Attribution 4.0 International License.
Journal article
Distributional equivalence and structure learning for bow-free acyclic path diagrams
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