Pillay, JasonBekker, Andriette, 1958-Ferreira, Johannes TheodorusArashi, Mohammad2025-10-162025-10-162025-05Pillay, J., Bekker, A., Ferreira, J. & Arashi, M. 2025, 'Soft computing for the posterior of a matrix t graphical network', International Journal of Approximate Reasoning, vol. 180, art. 109397, pp. 1-18, doi : 10.1016/j.ijar.2025.109397.0888-613X (print)1873-4731 (online)10.1016/j.ijar.2025.109397http://hdl.handle.net/2263/104746DATA AVAILABILITY : The authors do not have permission to share data.Modeling noisy data in a network context remains an unavoidable obstacle; fortunately, random matrix theory may comprehensively describe network environments. Noisy data necessitates the probabilistic characterization of these networks using matrix variate models. Denoising network data using a Bayesian approach is not common in surveyed literature. Therefore, this paper adopts the Bayesian viewpoint and introduces a new version of the matrix variate t graphical network. This model's prior beliefs rely on the matrix variate gamma distribution to handle the noise process flexibly; from a statistical learning viewpoint, such a theoretical consideration benefits the comprehension of structures and processes that cause network-based noise in data as part of machine learning and offers real-world interpretation. A proposed Gibbs algorithm is provided for computing and approximating the resulting posterior probability distribution of interest to assess the considered model's network centrality measures. Experiments with synthetic and real-world stock price data are performed to validate the proposed algorithm's capabilities and show that this model has wider flexibility than the model proposed by [13]. HIGHLIGHTS • Expanding the framework for denoising financial data inside the realm of graphical network theory, where the assumption of normality in the model is inadequate to account for the variation. • Introduction of the matrix variate gamma and inverse matrix variate gamma as priors for the covariance matrices; the univariate scale parameter β may be fixed or subject to a prior. • Following Bayesian inference with more flexible priors, there is an improvement based on relevant accuracy measures. • Experimental results indicate that our proposed framework and results outperform those of [13].en© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Adjacency matrixStock price dataPrecision matrixMatrix variate tMatrix variate gamma distributionGaussian graphical modelBayesian networkArticle