Results 1 to 10 of about 316,835 (263)
New Journal of Physics, 2014 Standard tomographic analyses ignore model uncertainty. It is assumed that a given model generated the data and the task is to estimate the quantum state, or a subset of parameters within that model.
Christopher Ferrie
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Bayesian Network Model Averaging Classifiers by Subbagging [PDF]
Entropy, 2022 When applied to classification problems, Bayesian networks are often used to infer a class variable when given feature variables. Earlier reports have described that the classification accuracy of Bayesian network structures achieved by maximizing the ...
Shouta Sugahara, Itsuki Aomi, Maomi Ueno
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clusterBMA: Bayesian model averaging for clustering [PDF]
PLoS ONE, 2023 Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature.
Owen Forbes +9 more
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Determination of Bio-Based Fertilizer Composition Using Combined NIR and MIR Spectroscopy: A Model Averaging Approach [PDF]
Sensors, 2022 Application of bio-based fertilizers is considered a practical solution to enhance soil fertility and maintain soil quality. However, the composition of bio-based fertilizers needs to be quantified before their application to the soil.
Khan Wali +4 more
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Nonlinear predictive model selection and model averaging using information criteria
Systems Science & Control Engineering, 2018 This paper is concerned with the model selection and model averaging problems in system identification and data-driven modelling for nonlinear systems. Given a set of data, the objective of model selection is to evaluate a series of candidate models and ...
Yuanlin Gu +2 more
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Combining Predictions of Auto Insurance Claims
Econometrics, 2022 This paper aims to better predict highly skewed auto insurance claims by combining candidate predictions. We analyze a version of the Kangaroo Auto Insurance company data and study the effects of combining different methods using five measures of ...
Chenglong Ye +5 more
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Frontiers in Energy Research, 2021 In view of the intrinsic complexity of the oil market, crude oil prices are influenced by numerous factors that make forecasting very difficult.
Bai Huang +6 more
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Model Averaging in Viral Dynamic Models [PDF]
The AAPS Journal, 2020 The paucity of experimental data makes both inference and prediction particularly challenging in viral dynamic models. In the presence of several candidate models, a common strategy is model selection (MS), in which models are fitted to the data but only results obtained with the "best model" are presented.
Gonçalves, Antonio +3 more
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Sensors, 2023 Previous studies investigating multi-sensor fusion for the collection of soil information have shown variable improvements, and the underlying prediction mechanisms are not sufficiently understood for spectrally-active and -inactive properties.
Isabel Greenberg +5 more
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A New Model Averaging Approach in Predicting Credit Risk Default
Risks, 2021 The paper introduces a novel approach to ensemble modeling as a weighted model average technique. The proposed idea is prudent, simple to understand, and easy to implement compared to the Bayesian and frequentist approach.
Paritosh Navinchandra Jha +1 more
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