Reliability modeling and analysis of communication networks
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TLDR
This is the first in-depth review of the application of reliability modeling and analysis techniques in communication networks and critically evaluate the pros and cons of different approaches.About:
This article is published in Journal of Network and Computer Applications.The article was published on 2017-01-15 and is currently open access. It has received 88 citations till now. The article focuses on the topics: Reliability block diagram & Reliability (statistics).read more
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References
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Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Model checking
TL;DR: Model checking tools, created by both academic and industrial teams, have resulted in an entirely novel approach to verification and test case generation that often enables engineers in the electronics industry to design complex systems with considerable assurance regarding the correctness of their initial designs.
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Bayesian Network Classifiers
TL;DR: Tree Augmented Naive Bayes (TAN) is single out, which outperforms naive Bayes, yet at the same time maintains the computational simplicity and robustness that characterize naive Baye.
Book
Finite Markov chains
John G. Kemeny,J. Laurie Snell +1 more
TL;DR: This lecture reviews the theory of Markov chains and introduces some of the high quality routines for working with Markov Chains available in QuantEcon.jl.