The paper introduces a maxitive Donsker-Varadhan formulation for possibilistic variational inference, deriving learning rules and CBOpt optimizers that achieve competitive performance on image classification.
Robust bayesian inference in complex models with possibility theory
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Introduces a principled decentralised possibilistic fusion rule proven asymptotically exact for the Bernoulli filter that maintains local posterior independence and outperforms probabilistic baselines in cardinality and localisation error.
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Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference
The paper introduces a maxitive Donsker-Varadhan formulation for possibilistic variational inference, deriving learning rules and CBOpt optimizers that achieve competitive performance on image classification.
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Decentralised possibilistic inference with applications to target tracking
Introduces a principled decentralised possibilistic fusion rule proven asymptotically exact for the Bernoulli filter that maintains local posterior independence and outperforms probabilistic baselines in cardinality and localisation error.