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Apr 7, 2024 · In this paper, we study the Bayesian inference in the framework of uncertainty theory based on the uncertain Bayesian rule developed by Lio and Kang in 2022.
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Apr 7, 2024 · In this paper, we study the Bayesian inference in the framework of uncertainty theory based on the uncertain. Bayesian rule developed by Lio and ...
May 26, 2024 · In Bayesian rule an unknown parameter is thought to be a quantity whose variation can be characterized by a prior distribution. Then some data ...
Striving for novel uncertainty analysis tools, we present the Bayesian calibration of process- based models as a methodological advancement that warrants ...
Goals: • Replace point estimates with distributions. • Construct credible and prediction intervals. • Natural in a Bayesian framework.
In that case, the inferences concerning uncertainty may be attributed to the theory of Bayesian statistics. Theoretically, the uncertainty of an event is ...
List of references · Berger JO (2013) Statistical decision theory and Bayesian analysis. · Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis.
This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and ...
In Bayesian rule an unknown parameter is thought to be a quantity whose variation can be characterized by a prior distribution. Then some data are observed ...
In Bayesian rule an unknown parameter is thought to be a quantity whose variation can be characterized by a prior distribution. Then some data are observed ...
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