Results 11 to 20 of about 1,563,284 (329)
Bayesian Model Averaging and Weighted Average Least Squares: Equivariance, Stability, and Numerical Issues [PDF]
SSRN Electronic Journal, 2011 In this article, we describe the estimation of linear regression models with uncertainty about the choice of the explanatory variables. We introduce the Stata commands bma and wals, which implement, respectively, the exact Bayesian model-averaging estimator and the weighted-average least-squares estimator developed by Magnus, Powell, and Prüfer (2010,
De Luca, G., Magnus, J.R.
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Bayesian Model Averaging for Propensity Score Analysis. [PDF]
Multivariate Behav Res, 2014 This article considers Bayesian model averaging as a means of addressing uncertainty in the selection of variables in the propensity score equation. We investigate an approximate Bayesian model averaging approach based on the model-averaged propensity score estimates produced by the R package BMA but that ignores uncertainty in the propensity score. We
Kaplan D, Chen J.
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What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization [PDF]
International Conference on Artificial Intelligence and Statistics, 2023 In this paper, we conduct a comprehensive study of In-Context Learning (ICL) by addressing several open questions: (a) What type of ICL estimator is learned by large language models?
Yufeng Zhang +3 more
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Model-averaged Bayesian t tests
Psychonomic Bulletin & Review, 2022 Abstract One of the most common statistical analyses in experimental psychology concerns the comparison of two means using the frequentist t test. However, frequentist t tests do not quantify evidence and require various assumption tests. Recently, popularized Bayesian t tests do quantify evidence, but these were developed for scenarios where
Maximilian Maier +7 more
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Bayesian model averaging for analysis of lattice field theory results [PDF]
Physical Review D, 2020 Statistical modeling is a key component in the extraction of physical results from lattice field theory calculations. Although the general models used are often strongly motivated by physics, their precise form is typically ill-determined, and many model
W. Jay, E. Neil
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Bayesian model averaging for mortality forecasting using leave-future-out validation [PDF]
International Journal of Forecasting, 2021 Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Various stochastic frameworks have been developed to model mortality patterns by taking into account the main stylized facts driving these patterns ...
Karim Barigou +3 more
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Probabilistic Solar Power Forecasting Using Bayesian Model Averaging
IEEE Transactions on Sustainable Energy, 2021 There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed.
K. Doubleday +3 more
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Predicting waste generation using Bayesian model averaging [PDF]
Global Journal of Environmental Science and Management, 2017 A prognosis model has been developed for solid waste generation from households in Hoi An City, a famous tourist city in Viet Nam. Waste sampling, followed by a questionnaire survey, was carried out to gather data.
M.G. Hoang +3 more
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Bayesian Model Weighting: The Many Faces of Model Averaging
Water, 2020 Model averaging makes it possible to use multiple models for one modelling task, like predicting a certain quantity of interest. Several Bayesian approaches exist that all yield a weighted average of predictive distributions. However, often, they are not
Marvin Höge, A. Guthke, W. Nowak
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Bayesian model‐averaged meta‐analysis in medicine [PDF]
Statistics in Medicine, 2021 We outline a Bayesian model‐averaged (BMA) meta‐analysis for standardized mean differences in order to quantify evidence for both treatment effectiveness and across‐study heterogeneity . We construct four competing models by orthogonally combining two present‐absent assumptions, one for the treatment effect and one for across‐study heterogeneity.
František Bartoš +5 more
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