tailieunhanh - Tài liệu Discrete-Event Simulation: A First Course

There has been much previous work on node localization and event detection [6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16] , includ- ing foundational work on theoretical lower bounds [17, 18]. Sex- tant differentiates itself from this body of work in several ways. First, it does not assume uniform transmission radii (. a unit disk graph) or symmetric connectivity; instead it extracts geographic constraints from the link layer based on a novel, realistic constraint extraction model that accommodates the large percentage of uni- directional links and non-uniform coverage areas encountered in practice. These constraints lead to non-convex solutions, which are typically much more accurate, though more complex,. | Section Discrete Random Variables Discrete-Event Simulation A First Course 2006 Pearson Ed. Inc. 0-13-142917-5 Discrete-Event Simulation A First Course Section Discrete Random Variables 1 27 Section Discrete Random Variables o A random variable X is discrete if and only if its set of possible values X is finite or at most countably infinite o A discrete random variable X is uniquely determined by a Its set of possible values X a Its probability density function pdf A real-valued function f defined for each x G X as the probability that X has the value x By definition f x Pr X x E f x 1 x Discrete-Event Simulation A First Course Section Discrete Random Variables 2 27 Examples o Example X is Equilikely a b X b a 1 and each possible value is equally likely f x b a 1 x a a 1 --- b o Example Roll two fair face If X is the sum of the two up faces X x x 2 3 . 12 From example f x 6 376 x 1 x 2 3 . 12 Discrete-Event Simulation A First Course Section Discrete Random Variables 3 .

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