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Websian framework with Markov chain Monte Carlo in R with JAGS accessed with the ‘JagsUI’ package [36–38]. We used uninformative priors for all analyses and standar-dized continuous variables to have a mean of 0 and a standard deviation of 1. We assessed model convergence by visually inspecting the chains, and with the Gelman– WebSian Jin, Sheng Di, Xin Liang, Jiannan Tian, Dingwen Tao, and Franck Cappello DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression ACM International Symposium on High-Performance Parallel and Distributed Computing, Phoenix, Arizona, USA, June 24–28, 2024 [paper] Websian approach to specifying this paper's particular informative prior. Section IV describes an empirical Bayes approach to estimating the adjustment factors asso? ciated with this informative prior. In section V, the simulation model and results are presented. Section VI contains concluding remarks. II. Estimation Risk in Portfolio Selection how much money is a jet ski