Bayesian probabilistic forecasting with large-scale educational trend data: a case study using NAEP

Large-scale Assessments in Education

An IERI – International Educational Research Institute Journal

Table 5 Summary of log-predictive scores and Kullback–Leibler divergences for boys and girls in the flexible priors setting

Values with * indicate that these analyses was based on fewer than the full 64 total possible models due to computational tolerances being reached in the calculation of likelihoods. In no case were fewer than 30 models used in the LPS and KLD calculations for these cases
EBL: Local empirical Bayes; HG-3: Hyper-g prior with \(\alpha = 3\); HG-4: Hyper-g prior with \(\alpha = 4\); HG-UIP: Hyper-g prior with UIP setting; HG-RIC: Hyper-g prior with RIC setting; HG-BRIC: Hyper-g prior with BRIC setting. Uniform: uniform model prior; Binomial (m = 2): Binomial model prior with model size = 2; Binomial (m = 4): Binomial model prior with model size = 4; Beta-binomial: Beta-binomial model prior