This paper presents two additional strategies to reduce simulation time for Bayesian based stopping rules in VHDL model verification. The first is that a semi-random variable is defined and the data staying in the semi-random variable range are skipped when stopping rule is running, and a turning point that can partition a random variable into a semi-random and a genuine random variable is chos...

A fuzzy random variable is viewed as the imprecise observation of the outcomes in a random experiment. Since randomness and vagueness coexist in the same framework, it seems reasonable to integrate fuzzy random variables into imprecise probabilities theory. Nevertheless, fuzzy random variables are commonly presented in the literature as classical measurable functions associated to a classical p...

Given a random variable X, we de ne a cumulative distribution function (cdf), FX : R → [0, 1], such that FX(t) = P{X ≤ t} for all t ∈ R. Here P{X ≤ t} denotes the probability that X ≤ t. To emphasize that random variable X has cdf FX , we write X ∼ FX . Note that FX(t) is a nondecreasing function of t. There are 3 types of random variables: discrete, continuous, and mixed. Discrete random varia...

In this paper a new method is introduced for generating uniform random numbers based on data oriented modeling. This modeling method of random variable is proposed in [1]. By using this method, uniform random variable is modeled with several data. Up to now, statisticians have used mathematical functions to model random variables. This approach conforms to human brain structure, in other words ...

‎in this paper‎, ‎using fixed point method‎, ‎we prove the generalized hyers-ulam stability of‎ ‎random homomorphisms in random $c^*$-algebras and random lie $c^*$-algebras‎ ‎and of derivations on non-archimedean random c$^*$-algebras and non-archimedean random lie c$^*$-algebras for‎ ‎the following $m$-variable additive functional equation:‎ ‎$$sum_{i=1}^m f(x_i)=frac{1}{2m}left[sum_{i=1}^mfle...

The expectation of a random variable is its average value, where each value is weighted according to the probability that it comes up. The expectation is also called the expected value or the mean of the random variable. For example, suppose we select a student uniformly at random from the class, and let R be the student’s quiz score. Then E [R] is just the class average —the first thing everyo...

A regular variable holds, at any one time, a single value, be it numeric or otherwise. In contrast, a random variable (RV) holds a distribution over values, be they numeric or otherwise. For example, the outcome of a future toss of a fair coin can be captured by a random variable X holding the following distribution: ’HEAD’ with probability 0.5, and ’TAIL’ with probability 0.5 . We will use upp...

A wrapper variable selection procedure is proposed for use with learning machines that generate a measure of variable importance, such as Random Forest. The procedure is based on iteratively removing low-ranking variables and assessing the learning machine performance by cross-validation. The procedure is implemented for Random Forest on some QSAR modeling examples from drug discovery and devel...