A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical Models

In this paper, we propose to derive an approximate theoretical distribution under the null hypothesis of the Low-Rank Adaptive Normalized Matched Filter (LR-ANMF).. This detector

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des

We perform numerical experiments to assess the performance of our approach (SIMoNe, Statistical Inference for Modular Network, Chiquet et al. 2009) and compare it to already

The second paper [9] deals with general U-statistics of functions of β-mixing sequences, for either two independent samples, or for two... adjacent samples from the same time

However, this strategy would force us to define metamorphic relations for each operation meanwhile our approach can be used generically to generate test data for any analysis

Inference algorithms and a new optimization scheme are proposed, that allow the learning of integer parameters without the need for any floating point compu- tation.. This opens up

The construction of a simulation model sometimes lacks in the formalization process, needed to understand the structure and the behavior of the model before any specific

As both the null and alternative hypotheses are composite hypotheses (each comprising a different set of distributions), computing the value of the generalized likelihood ratio