discrete random variable

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dis·crete ran·dom var·i·a·ble

a random variable that may assume a countable number of values, each with a probability strictly greater than zero.
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The first version uses discrete variables, while the second version uses continuous variables.
We cast this problem as the joint optimization of discrete variables indicating which constraints apply, and continuous variables representing the 3D model that best satisfies these constraints.
The purpose of a Parametrized Correlation Method is to determine a relationship between two sets of stochastic discrete variables.
The efficiency of the proposed optimizer was tested via the optimization of several truss structures with discrete variables (Ali et al, 2012).
In this type of programming, both continuous and discrete variables such as 0-1 variables (the most commonly used) are employed.
In response, we have experimented with measuring discrete variables of behavior, developing distance learning programs, evaluating employee morale, reengineering processes, moving training to be just-in-time, and a barrage of other well-meaning efforts; always remaining focused on improving how we perform, while rarely defining and measuring the required performance.
Therefore we recommend use of discrete variables in resource constrained settings, in spite of their statistical limitations.
DataMontage enhances data review, decision support, and real-time monitoring systems for diverse applications in which researchers, engineers, planners, and analysts must rapidly detect temporal patterns spanning many continuous and discrete variables and events in order to interpret complex data and reach time-critical decisions.
Thus, the mentioned problem is a nonlinear optimization problem with a combination of continuous and discrete variables (MINLP).
The Genetic algorithm is not continuous because of the discrete variables shading dimensions.
For discrete variables, a conditional probability table (CPT) can be used, where a child node can take different values for each combination of values from its parents.
Mike developed a model he called the Qual-VAR that combines discrete variables such as the business cycle phase with the commonly used vector autoregression (VAR).
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