Název: Block-Coordinate Descent Algorithm for Interventional Data in Directed Graphical Models
Autoři: Wu, Jun
Typ dokumentu: Příspěvky z konference
Konference/Akce: Workshop on Uncertainty Processing - WUPES 2025 /13./, Třešť (CZ), 20250604
Rok: 2025
Jazyk: eng
Abstrakt: Computing maximum likelihood estimates in linear structural equation models is generally a difficult problem. The critical equations are usually non-linear and have numerous solutions, even for purely observational data. The block-coordinate descent (BCD) algorithm proposed by Drton et al. (2019)[1] is an efficient way to solve the optimization problem by decomposing it into a series of sub-problems with closed-form solutions, and which works with observational data. In this work, we describe the general problem of a BCD-type scheme for computing maximum likelihood estimates in linear structural equation models without hidden variables, integrating multiple observational and interventional environments. With interventional data, the degrees of both the original likelihood equations and the block-coordinate update equations could increase greatly. We study special setups in which the block optimization subproblems have a degree of at most 2 and provide closed-form solutions in these cases. Additionally, we discuss the potential applications of the model and algorithm to health and well-being data.\n
Číslo projektu: EH22_008/0004583
Poskytovatel projektu: GA MŠk
Zdrojový dokument: Proceedings of the 13th Workshop on Uncertainty Processing (WUPES’25), ISBN 978-80-7378-525-3

Instituce: Ústav informatiky AV ČR (web)
Informace o dostupnosti dokumentu: Dokument je dostupný v příslušném ústavu Akademie věd ČR.
Původní záznam: https://hdl.handle.net/11104/0367844

Trvalý odkaz NUŠL: http://www.nusl.cz/ntk/nusl-684948


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