This research introduces a new constraint domain for reasoning about data with uncertainty. It extends convex modeling with the notion of p-box to gain additional quantifiable information on the data whereabouts. Unlike existing approaches, the p-box envelops an unknown probability instead of approximating its representation. The p-box bounds are uniform cumulative distribution functions (cdf) ...

In many practical situations, we need to select one of the two alternatives, and we do not know the exact form of the user’s utility function – e.g., we only know that it is increasing. In this case, stochastic dominance result says that if the cumulative distribution function (cdf) corresponding to the first alternative is always smaller than or equal than the cdf corresponding to the second a...

In all systems simulation, random variates are considered as a main factor and based of simulation heart. Actually, randomization is inducted by random variates in the simulation. Due to the importance of such a problem, a new method for generation of random variates from continuous distributions is presented in this paper. The proposed algorithm, called uniform fractional part (UFP) is simpler...

This paper presents an analytical framework to study search strategies in large-scale decentralized unstructured peer-to-peer (P2P) networks. The peers comprising the P2P network and their application-level connections are modeled as Generalized Random Graphs (GRGs) whose simple and efficient analysis is accomplished using the generating function of the graph’s degree distribution. The framewor...

We present a novel nonparametric adaptive partitioning and stitching (NAPS) algorithm to estimate probability density function (PDF) of single variable. Sampled data is partitioned into blocks using branching tree that minimizes deviations from uniform within various sample sizes arranged in staggered format. The block are constructed balance the load parallel computing as PDF for each independ...

A cumulative distribution function (CDF) states the probability that a sample of a random variable will be no greater than a value x, where x is a real value. Closed form expressions for important CDFs have parameters, such as mean and variance. If these parameters are not point values but rather intervals, sharp or fuzzy, then a single CDF is not specified. Instead, a family of CDFs is specifi...

This paper solves the problem of estimation of the parameters of a hyperexponential density and presents a practical application of the solution in sensor networks. Two novel algorithms for estimating the parameters of the density are formulated. In the first algorithm, an objective function is constructed as a function of the unknown component means and an estimate of the cumulative distributi...

In this letter, we derive the CDF (cumulative distribution function) of k th contact distance (CD) and nearest neighbor (NND) xmlns:xlink="http://www.w3.org/1999/xlink"> n -dimensional ( xmlns:xlink="http://www.w3.org/1999/xlink">n -D) Matérn cluster process (MCP). We present a new approach ...

The paper presents a model that extends the stochastic finite element method to the modelling of transitional energetic–statistical size effect in unnotched quasibrittle structures of positive geometry (i.e. failing at the start of macro-crack growth), and to the low probability tail of structural strength distribution, important for safe design. For small structures, the model captures the ene...