Titlebook: Statistical Analysis of Graph Structures in Random Variable Networks; V. A. Kalyagin,A. P. Koldanov,P. M. Pardalos Book 2020 The Author(s)

滋养

Statistical Analysis of Graph Structures in Random Variable Networks

纹章

SpringerBriefs in Optimizationhttp://image.papertrans.cn/s/image/876337.jpg

帽子

https://doi.org/10.1007/978-3-030-60293-2network analysis; network structure identification; robust statistical procedures; statistical procedur

mechanical

Conclusion,In this book, we presented the first steps to investigate uncertainty of network structure identification in random variable networks. We propose to measure uncertainty as a risk (expected value of losses)associated witha loss function. On this basis we introduce the concepts of robustness and optimality of identification procedures.

连累

Statistical Analysis of Graph Structures in Random Variable Networks978-3-030-60293-2Series ISSN 2190-8354 Series E-ISSN 2191-575X

FOLLY

V. A. Kalyagin,A. P. Koldanov,P. A. Koldanov,P. M. Pardalos their presentation. Actually, a retrospective research revealed about 6% KD patients consulted gastroenterologist initially (Ohnishi et al., J Med Ultrason (2001) 45(2):381–384, 2018). Their median duration of illness at gastroenterology consultation was 5 days, whereas median duration of illness a

Modify

Introduction,ntification. This approach allows to study general statistical properties (unbiasedness, optimality) of different identification algorithms and to design a new class of robust identification algorithms. Large area of applications varies from market network analysis to gene network analysis.

AVOW

Network Structure Identification Algorithms,ss the problem of identification of concentration graph. In addition, we describe some algorithms for MST and PMFG identification. We use numerical simulations to illustrate described algorithms and discuss the results.

摇晃

Robustness of Network Structure Identification,sue, we consider the sign similarity network, introduce a new class of network structure identification algorithms, and prove its robustness in the class of elliptical distributions. We show how to use these algorithms to construct robust network structure identification algorithms in other correlation networks.

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