CostaL AnacletoO NascimentoDC SmithJQ QueenCM LouzadaF NicholsTDiego Nascimento2025-06-052025-06-0520240031-3203https://hdl.handle.net/20.500.12740/22642In graph theory, complex structures are studied, as well as the dynamics of the connectivity strength of this structure. However, in the estimation procedure, particular characteristics need to be considered at some level as informative when estimating the characteristics of a group. This work proposes a model that provides dynamic estimation of the network structure based on a model that makes it possible to incorporate hierarchy (individual information) in the process. In addition, we show the feasibility of modeling a complex structure by levels, exemplifying this by cluster analysis as a visualization of the embedding projection reduction space. Our case study is a neuroscience experiment, which needs to estimate the brain connectivity map, that is, to study the information flow of the brain in resting-stage subjects. Methods for estimating group networks can be grouped into the following 4 categories: group-structure (GS), virtual-typical-subject (VTS), commonstructure (CS), and individual-structure (IS). These four group-structure estimation methods were compared in the context of the Multiregression Dynamic Models. Results showed that the proposed Bayesian Network Structure Dynamic estimation, using GS and hierarchical dynamic models, accommodates the latent/personal information in the estimation process by extracting the pattern shared between them. Moreover, the cluster analysis estimation corroborates the empirical results and expert judgments. C1 [Costa, Lilia] Univ Fed Bahia, Inst Math & Stat, Salvador, Brazil. [Anacleto, Osvaldo; Louzada, Francisco] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, Brazil. [Nascimento, Diego C.] Univ Atacama, Dept Matemat, Copiapo, Chile. [Smith, James Q.] Univ Warwick, Coventry, England. [Queen, Catriona M.] Open Univ, Milton Keynes, England. [Nichols, Thomas] Oxford Big Data Inst, Li Ka Shing Ctr Hlth Informat & Discovery, Oxford, England. [Nichols, Thomas] Univ Oxford, Nuffield Dept Populat Hlth, Oxford, England. C3 Universidade Federal da Bahia; Universidade de Sao Paulo; Universidad de Atacama; University of Warwick; Open University - UK; University of Oxfordinfo:eu-repo/semantics/closedAccessMultiregression dynamic modelBayesian networkGroup analysisCluster analysisHierarchical modelsComputer Science; EngineeringArticulo de revista10.1016/j.patcog.2024.110687