Results 281 to 290 of about 1,563,284 (329)
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Improved information criteria for Bayesian model averaging in lattice field theory
Physical Review D, 2022Bayesian model averaging is a practical method for dealing with uncertainty due to model specification. Use of this technique requires the estimation of model probability weights.
E. Neil, J. W. Sitison
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Predicting Hydrological Drought With Bayesian Model Averaging Ensemble Vine Copula (BMAViC) Model
Water Resources Research, 2022Streamflow deficit (hydrological drought) poses a large risk to water resources management, agricultural production, water supply, hydropower generation, and ecosystem services.
Haijiang Wu +3 more
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Journal of Environmental Management, 2021
Bayesian Model Averaging (BMA) is used to study inherent uncertainties using the Basic DRASTIC Framework (BDF) for assessing the groundwater vulnerability in a study area related to Lake Urmia.
Maryam Gharekhani +4 more
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Bayesian Model Averaging (BMA) is used to study inherent uncertainties using the Basic DRASTIC Framework (BDF) for assessing the groundwater vulnerability in a study area related to Lake Urmia.
Maryam Gharekhani +4 more
semanticscholar +1 more source
Bayesian Model Averaging for Spatial Econometric Models [PDF]
SSRN Electronic Journal, 2006 We extend the literature on Bayesian model comparison for ordinary least‐squares regression models to include spatial autoregressive and spatial error models. Our focus is on comparing models that consist of different matrices of explanatory variables. A Markov Chain Monte Carlo model composition methodology labeled MC3 by Madigan and York is developed
Olivier Parent, James P. Lesage
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2019
Bayesian model averaging (BMA) is a statistical method to rigorously take model uncertainty into account. This chapter gives a coherent overview on the statistical foundations and methods of BMA and its usefulness for forecasting, but also for the identification of robust determinants. The focus is given on economic applications.
Mevin B. Hooten, Trevor J. Hefley
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Bayesian model averaging (BMA) is a statistical method to rigorously take model uncertainty into account. This chapter gives a coherent overview on the statistical foundations and methods of BMA and its usefulness for forecasting, but also for the identification of robust determinants. The focus is given on economic applications.
Mevin B. Hooten, Trevor J. Hefley
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Bayesian model averaging of longitudinal dose-response models
Journal of Biopharmaceutical Statistics, 2023Selecting a safe and clinically beneficial dose can be difficult in drug development. Dose justification often relies on dose-response modeling where parametric assumptions are made in advance which may not adequately fit the data. This is especially problematic in longitudinal dose-response models, where additional parametric assumptions must be made.
Richard D, Payne +2 more
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A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning
, 2020In this paper, a hybrid wind power forecasting approach based on Bayesian model averaging and Ensemble learning (BMA-EL) is proposed. Firstly, SOM clustering and K-fold cross-validation are used to generate multiple sets of the training subsets with the ...
Wang Gang +3 more
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Bayesian Model Averaging for Ligand Discovery
Journal of Chemical Information and Modeling, 2009High-throughput screening (HTS) is now a standard approach used in the pharmaceutical industry to identify potential drug-like lead molecules. The analysis linking biological data with molecular properties is a major goal in both academic and pharmaceutical research.
Nicos, Angelopoulos +2 more
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Bayesian modeling of multivariate average bioequivalence
Statistics in Medicine, 2007AbstractBioequivalence trials are usually conducted to compare two or more formulations of a drug. Simultaneous assessment of bioequivalence on multiple endpoints is called multivariate bioequivalence. Despite the fact that some tests for multivariate bioequivalence are suggested, current practice usually involves univariate bioequivalence assessments ...
Pulak, Ghosh, Mithat, Gönen
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Introducing entropy-based Bayesian model averaging for streamflow forecast
, 2020Bayesian Model Averaging (BMA) is a well-known statistical post-processing approach for probabilistically merging individual forecasts. In BMA, the posterior distribution of the predictand variable is determined by implementing the law of total ...
Pedram Darbandsari, P. Coulibaly
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