A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors

@article{Pfeuffer2016ABP,
  title={A Bounded p-norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors},
  author={Julianus Pfeuffer and Oliver Serang},
  journal={Journal of Machine Learning Research},
  year={2016},
  volume={17},
  pages={36:1-36:39}
}
Max-convolution is an important problem closely resembling standard convolution; as such, max-convolution occurs frequently across many fields. Here we extend the method with fastest known worst-case runtime, which can be applied to nonnegative vectors by numerically approximating the Chebyshev norm ‖ · ‖∞, and use this approach to derive two numerically stable methods based on the idea of computing pnorms via fast convolution: The first method proposed, with runtime in O(k log(k) log(log(k… CONTINUE READING
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