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Albert, Jim; Hu, Jingchen – Journal of Statistics Education, 2020
Bayesian statistics has gained great momentum since the computational developments of the 1990s. Gradually, advances in Bayesian methodology and software have made Bayesian techniques much more accessible to applied statisticians and, in turn, have potentially transformed Bayesian education at the undergraduate level. This article provides an…
Descriptors: Bayesian Statistics, Computation, Statistics Education, Undergraduate Students
Chiu, Chia-Yi; Köhn, Hans-Friedrich; Wu, Huey-Min – International Journal of Testing, 2016
The Reduced Reparameterized Unified Model (Reduced RUM) is a diagnostic classification model for educational assessment that has received considerable attention among psychometricians. However, the computational options for researchers and practitioners who wish to use the Reduced RUM in their work, but do not feel comfortable writing their own…
Descriptors: Educational Diagnosis, Classification, Models, Educational Assessment
Peer reviewedVos, Hans J. – Journal of Educational and Behavioral Statistics, 1999
Formulates optimal sequential rules for mastery testing using an approach derived from Bayesian sequential decision theory to consider both threshold and linear loss structures. Adopts the binomial probability distribution as the psychometric model. Provides an empirical example for concept-learning in medicine. (SLD)
Descriptors: Bayesian Statistics, Equations (Mathematics), Mastery Tests, Probability
Lavine, Michael – 1987
A specific application of a general paradigm described by R. D. Cook (1986) and R. McCulloch (1985) in assessing local influence is given. Snow geese flock size is estimated as "X" by an observer and "Y" by a photograph. "Y" is believed to be the true flock size. The problem is to obtain true flock size "Z"…
Descriptors: Bayesian Statistics, Equations (Mathematics), Predictive Measurement, Sample Size
Peer reviewedMaris, Gunter; Maris, Eric – Psychometrika, 2002
Introduces a new technique for estimating the parameters of models with continuous latent data. To streamline presentation of this Markov Chain Monte Carlo (MCMC) method, the Rasch model is used. Also introduces a new sampling-based Bayesian technique, the DA-T-Gibbs sampler. (SLD)
Descriptors: Bayesian Statistics, Equations (Mathematics), Estimation (Mathematics), Markov Processes

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