English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Linear structural equation models, which relate random variables via linearinterdependencies and Gaussian noise, are a popular tool for modelingmultivariate joint distributions. These models correspond to mixed graphs thatinclude both directed and bidirected edges representing the linearrelationships and correlations between noise terms, respectively. A question ofinterest for these models is that of parameter identifiability, whether or notit is possible to recover edge coefficients from the joint covariance matrix ofthe random variables. For the problem of determining generic parameteridentifiability, we present an algorithm that extends an algorithm from priorwork by Foygel, Draisma, and Drton (2012). The main idea underlying our newalgorithm is the use of ancestral subsets of vertices in the graph inapplication of a decomposition idea of Tian (2005).

Generic Identifiability of Linear Structural Equation Models by Ancestor Decomposition