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Bayesian Psychometric Modeling
Measurement: Interdisciplinary Research and Perspectives, 2018ABSTRACTThis article aims to review the book “Bayesian psychometric modeling” by Levy and Mislevy (2016). It provides an overview of the book content and explains the significance of Bayesian psychometric modeling issues that were explored in the book. Both strengths and weaknesses of the work are discussed.
Xin Qiao, Hong Jiao
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Bayesian nonparametric hierarchical modeling
Biometrical Journal, 2009AbstractIn biomedical research, hierarchical models are very widely used to accommodate dependence in multivariate and longitudinal data and for borrowing of information across data from different sources. A primary concern in hierarchical modeling is sensitivity to parametric assumptions, such as linearity and normality of the random effects ...
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COSMOLOGICAL BAYESIAN MODEL SELECTION
Statistical Problems in Particle Physics, Astrophysics and Cosmology, 2006Bayesian model comparison can be used to decide whether the introduction of a new parameter is warranted by data. I focus on the Savage-Dickey density ratio as a method to compute the Bayes factor of nested models without carrying out a computationally demanding multi-dimensional integration.
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Proceedings of the 50th Annual Design Automation Conference, 2013
Efficient high-dimensional performance modeling of today's complex analog and mixed-signal (AMS) circuits with large-scale process variations is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian Model Fusion (BMF).
Fa Wang +4 more
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Efficient high-dimensional performance modeling of today's complex analog and mixed-signal (AMS) circuits with large-scale process variations is an important yet challenging task. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian Model Fusion (BMF).
Fa Wang +4 more
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Bayesian statistics and modelling
Nature Reviews Methods Primers, 2021Rens van de Schoot +2 more
exaly
2018
We provide an overview of Bayesian model averaging (BMA), starting with a summary of the mathematics associated with classical BMA, including the calculation of posterior model probabilities and the choice of priors for both the models and the model parameters.
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We provide an overview of Bayesian model averaging (BMA), starting with a summary of the mathematics associated with classical BMA, including the calculation of posterior model probabilities and the choice of priors for both the models and the model parameters.
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