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Showing 1 to 5 of 5 for “"Belief functions"”.

  1. Generative modelling under epistemic uncertainty

    … shifting from point-estimate probabilities to belief functions over the power set of outcomes. Enabled by a novel Budgeting strategy that ensures scalability, this framework allows models to explicitly represent ignorance. While we validate these principles through Random-Set Neural Networks …

    oxford-brookes Repository record for Generative modelling under epistemic uncertainty (opens in a new tab)

  2. Imprecise probability analysis for integrated assessment of climate change

    … and provide an algorithm to construct a belief function for the prior parameter uncertainty from a set of probability constraints that can be deduced from the literature or observational data. For the purpose of updating the prior with the likelihood function, we establish a …

    potsdam-diss Repository record for Imprecise probability analysis for integrated assessment of climate change (opens in a new tab)

  3. Ranking Aggregation Based on Belief Function Theory

    … quality. We propose a novel solution called Belief Ranking Estimator (BRE) that takes into account two aspects still unexplored in ranking combination: the approximation quality of the experts and for the first time the uncertainty related to each item position in the ranking. BRE estimates …

    trento Repository record for Ranking Aggregation Based on Belief Function Theory (opens in a new tab)

  4. Epistemic deep learning : enabling machine learning models to ‘know when they do not know’

    … that leverages random set theory to predict belief functions over sets of classes, capturing the extent of epistemic uncertainty through the width of associated credal sets, and demonstrating superior performance in terms of robustness and out-of-distribution detection compared to traditional …

    oxford-brookes Repository record for Epistemic deep learning : enabling machine learning models to ‘know when they do not know’ (opens in a new tab)

  5. Extended Entropy Maximisation and Queueing Systems with Heavy-Tailed Distributions

    Numerous studies on Queueing systems, such as Internet traffic flows, have shown to be bursty, self-similar and/or long-range dependent, because of the heavy (long) tails for the various distributions of interest, including intermittent intervals and queue lengths. Other studies have addressed …

    bradford Repository record for Extended Entropy Maximisation and Queueing Systems with Heavy-Tailed Distributions (opens in a new tab)