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Shallow Learning Techniques for Early Detection and Classification of Cyberattacks over MQTT IoT Networks. [PDF]

open access: yesSensors (Basel)
Díaz-Longueira A   +5 more
europepmc   +1 more source

Multidimensional Predictors of Functional Recovery After Curative Colon Cancer Surgery: A Patient-Reported Outcomes Study. [PDF]

open access: yesCureus
Lodi MN   +10 more
europepmc   +1 more source
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Optional Sampling Theorem for Deformed Submartingales

Theory of Probability and Its Applications, 2015
Summary: The family \({{\mathbb Q}}=(Q^{(n)}, {\mathcal F}_{n})_{n=0}^{\infty}\) of probability measures \(Q^{(n)}\) defined on \({{\mathcal F}}_{n}\) is considered. It is called a deformation of the first kind if for all \(n\in \{0,1,2,\dots\}\), \(Q^{(n+1)}|_{{{\mathcal F}}_{n}}\ll Q^{(n)}\), and a deformation of the second kind if \(Q^{(n+1)}|_ ...
I V Pavlov
exaly   +3 more sources

Generalization of Doob's optional sampling theorem for deformed submartingales

Russian Mathematical Surveys, 2013
I V Pavlov, O V Nazarko
exaly   +2 more sources

Randomized Stopping Times: DOOB'S Optiomal Sampling Theorem and Optimal Stopping

Mathematische Nachrichten, 1984
Consider an optimal stopping problem on a stochastic process \(\{Z_ n\}\), \(n\in N\); a class of randomized stopping times, which includes the class of stopping times, is introduced. First, a generalization of Doob's optional sampling theorem is given for randomized stopping times.
Buckdahn, R., Engelbert, H. J.
openaire   +2 more sources

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