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Statistical Decision Theory and Bayesian Analysis

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TLDR
An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory.
Abstract
1. Basic concepts 2. Utility and loss 3. Prior information and subjective probability 4. Bayesian analysis 5. Minimax analysis 6. Invariance 7. Preposterior and sequential analysis 8. Complete and essentially complete classes Appendices.

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Multi-kernel optimized relevance vector machine for probabilistic prediction of concrete dam displacement

TL;DR: This study proposes a novel probabilistic prediction approach for concrete dam displacement based on optimized relevance vector machine (ORVM), and lays the foundation for the application of RVM in the field of dam health monitoring.
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Utilization relevance vector machine for slope reliability analysis

TL;DR: This study shows that the proposed RVM-based FOSM is viable alternative for slope reliability analysis.
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Steps to implement Bayesian input distribution selection

TL;DR: It is shown that responses to questions like those already asked and answered in practice can be used to develop prior distributions for a wide class of models.
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Sequential decision making with partially ordered preferences

TL;DR: New insights and novel algorithms for strategy selection in sequential decision making with partially ordered preferences; that is, where some strategies may be incomparable with respect to expected utility are presented.
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Learning optimal decisions with confidence.

TL;DR: A rule is derived for learning a near-optimal linear combination of DDM inputs based on trial-by-trial feedback that learns not only the mean of the weights but also the uncertainty around this mean in the form of a covariance matrix, which provides insights into how a priori biases modulate the mechanisms leading to optimal decisions in diffusion models.