Distributed SPS Algorithms for Non-Asymptotic Confidence Region Evaluation

To summarize, this study shows that, when dealing with large sensor networks, iterative local average decisions play an important role on the achieved global performances, namely

Parallel to what was proposed for the concentration- based thresholds in Section 2.5, one can, as a first solution, consider a block-wise Rademacher resampling scheme, or,

We argue that uncertainty can be minimized through confidence (information evidence) and self-confidence (source agent) processing, The results here seek to enrich the

Section 3 shows how interval analysis can be used to characterize the exact confidence regions provided by LSCR in a global and guaranteed way.. Examples are treated in Section 4,

In this paper we propose a novel approach to evaluate the true performance of BCI systems based on Receiver Operating Characteristic (ROC) analysis, that removes the lim- itations

1 compares the results of source localization from RSS using a classical ML approach with CRB evaluation and NACR evaluation defined by LSCR.. A square region of 30m×30m

In this paper, we introduce the Distributed Upper Confidence Tree (DUCT) algorithm for solving such distributed constraint optimisation problems (DCOP).. DUCT works by first defining

In this paper, we discuss the problem of adaptive confidence balls, from a non-asymptotic point of view, in the particular context of density