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Showing 1 to 12 of 12 for “"prior probabilities"”.
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Quantization of prior probabilities in Bayesian group decision-making
… testing, a decision is made based on a prior probability distribution over the hypotheses, an observation with a known conditional distribution given the true hypothesis, and an assignment of costs to different types of errors. In a setting with multiple agents and the principle of "one …
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A method to establish non-informative prior probabilities for risk-based decision analysis
… uncertainty and risk are accounted for with probabilities for the possible states, or states of nature, that affect the outcome of a decision. Application of Bayes’ theorem requires non-informative prior probabilities, which represent the probabilities of states of nature for a decision maker …
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Comparisons of Improvement-Over-Chance Effect Sizes for Two Groups Under Variance Heterogeneity and Prior Probabilities
… separation, variance pattern, sample size, and prior probabilities. None of the indices provided acceptable estimates of effect for all the conditions examined. There were only a small number of conditions under which both accuracy and precision were acceptable. The results indicate that the …
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Aggregation and influence in teams of imperfect decision makers
Bayesian hypothesis testing inevitably requires prior probabilities of hypotheses. Motivated by human decision makers, this thesis studies how binary decision making is performed when the decision-making agents use imperfect prior probabilities. Three detection models with multiple agents are …
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Bayesian belief networks to integrate monitoring evidence of water distribution system contamination
… contaminant transport are used to estimate the prior probabilities of a positive sensor detection. These simulations are run over multiple combinations of possible source locations and initial mass injections for a conservative solute. This approach provides insight into the effect of …
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Frugal hypothesis testing and classification
… proposed for the quantization or clustering of prior probabilities appearing in the thresholds of likelihood ratio tests. This distortion is given the name mean Bayes risk error (MBRE). The quantization framework is extended to model human decision making and discrimination in segregated …
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Linear discriminant analysis
… three levels: (1.1) (a) with loss functions and prior probabilities, (b) without loss functions but with prior probabilities, (c) with neither. The first level leads to consideration of risks which may be split into two components, one for each type of misclassification, i.e. classification of an …
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"Whether God exists"
… and it is found that we can't fix the prior probabilities that Swinburne needs, not even by his appeal to simplicity. We then explain that this undermines P-inductive arguments, in particular, the cosmological argument. Then we consider whether theism offers a good explanation for the …
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A Genetic Bayesian Approach for Texture-Aided Urban Land-Use/Land-Cover Classification
… yielded 12% accuracy improvement by optimizing prior probabilities with the genetic algorithm; whereas the integrated approach with a texture vector and the genetic Bayesian classifier significantly improved classification accuracy by 17-21%. Compared to the neural network classifier and genetic …
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Identification of relevant predictors of loan default using the Elastic Net model
… of non-defaulting firms is constructed using prior probabilities based on the bond default rate for each year. The 278 potential variables, including the ten economic indicators and 268 financial ratios or summary indicators, are regularised with the application of the Elastic Net model. This …
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Knowing Who to Trust and What to Believe in the Presence of Conflicting Information
… is not objective and instead depends upon one's prior knowledge and beliefs, a philosophical point with deep practical implications. First, however, we must consider the even more basic question of how the trustworthiness of an information source can be expressed: measuring the trustworthiness of …
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Locally Adaptive Protocols for Quantum State Discrimination
… of a quantum system with corresponding prior probabilities $p_{k} = \text{Pr}[\rho = \rho_{k}]$. In the general case, although semi-definite programming offers a way to numerically approximate the optimal solution~\cite{Eldar_Semidefinite2}, a closed-form analytical solution for the …