We present a contribution to the current debate on whether it is more appropriate to fit a Gumbel distribution to the time series of the extreme dynamic pressures (i.e. of the squares of the extreme wind speeds) than to fit an extreme value distribution to the time series of the extreme wind speeds themselves. It has been shown that the use of time series of the extreme dynamic pressures would ...

The gapped local alignment score of two random sequences follows a Gumbel distribution. If computers could estimate the parameters of the Gumbel distribution within one second, the use of arbitrary alignment scoring schemes could increase the sensitivity of searching biological sequence databases over the web. Accordingly, this article gives a novel equation for the scale parameter of the relev...

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In this paper we consider the Bayesian estimators for the unknown parameters of Gumbel type-II distribution. The Bayesian estimators cannot be obtained in closed forms. Approximate Bayesian estimators are computed using the idea of Lindley’s approximation under different loss functions. The approximate Bayes estimates obtained under the assumption of non-informative priors are compared with the...

In this paper, we propose a bivariate extension of univariate composite (two-spliced) distributions defined by Pareto distribution for values larger than some thresholds and Gumbel on the complementary domain. The purpose is to capture behavior data consisting mainly small medium but also extreme values. Some properties proposed are presented. Further, two estimation procedures discussed illust...

We continue our study of exponential law for occurrences and returns of patterns in the context of Gibbsian random fields. For the low temperature plus phase of the Ising model, we prove exponential laws with error bounds for occurrence, return, waiting and matching times. Moreover we obtain a Poisson law for the number of occurrences of large cylindrical events and a Gumbel law for the maximal...

Permutations and matchings are core building blocks in a variety of latent variable models, as they allow us to align, canonicalize, and sort data. Learning in such models is difficult, however, because exact marginalization over these combinatorial objects is intractable. In response, this paper introduces a collection of new methods for end-to-end learning in such models that approximate disc...

Previous neural machine translation models used some heuristic search algorithms (e.g., beam search) in order to avoid solving the maximum a posteriori problem over translation sentences at test time. In this paper, we propose the Gumbel-Greedy Decoding which trains a generative network to predict translation under a trained model. We solve such a problem using the GumbelSoftmax reparameterizat...