Tag: Decision theory
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Decision Theory for Large-Scale Outlier Detection Using Aleatoric Uncertainty
The content discusses aleatoric uncertainty in Bayesian neural networks and its application to outlier detection. By leveraging decision theory, the author explores how modeling uncertainties in parameters and data generating mechanisms can enhance outlier classification. This involves formulating loss functions and employing Bayesian false discovery rate strategies for effective threshold setting.
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Bayesian Decision Theory for Gaussian Process (GP) Models with an Application Towards Approximate Evaluation of Source Functions Generating the GP as a Solution to a Differential Equation.
The author explores the integration of decision theory within the framework of Gaussian processes, focusing on nonparametric models. They highlight the relevance of selecting appropriate loss functions when applying Bayesian decision principles, particularly in the context of ordinary differential equations. Applications and future exploration in financial modeling and clustering are also suggested.