2026-10-14T04:38:58Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33724090 2026-09-14T08:55:43Z portal_1197 item_type_3 month_year_09_2026

Data for The correlation space of Gaussian latent tree models and model selection without fitting Chris G. Knight (24871714) Gaussian distribution Latent variables Migrated from ePrints We provide a complete description of possible distributions consistent with any Gaussian latent tree model. This description consists of polynomial equations and inequalities involving covariances between the observed variables. Testing inequality constraints can be done using the inverse Wishart distribution and this leads to simple preliminary assessment of tree-compatibility. To test equality constraints we employ general techniques of tetrad analyses. This approach is effective even for small sample sizes and can be easily adjusted to test either entire models or just particular macrostructures of a tree. Our methods are simple to implement and do not require fitting of the model. The versatility of the techniques is illustrated by performing exploratory and confirmatory tetrad analyses in linguistic and biological settings respectively.<br><br>Optical density of yeast species when observed in various environments. Key: ‘grotreatment’ is one of 96 environments, ‘pretreatment’ is ‘None’ in all cases, ‘Well’ indicates the spatial layout, ‘time_h' is hours of growth and ‘value’ is the optical density (which has a different baseline for each environment). 2016-07-28T00:00:00Z Dataset Dataset 10.82444/warw.33724090.v1 https://figshare.com/articles/dataset/Data_for_The_correlation_space_of_Gaussian_latent_tree_models_and_model_selection_without_fitting/33724090 CC BY-NC-SA 4.0