Karagöz, Yalçin
January 2003
Ekev Academic Review;Winter2003, Vol. 7 Issue 14, p311
Academic Journal
This paper presents general information on simulation with Pareto distribution and introduces an algorithm to generate random numbers by simulation method. A computer code has been developed for this algorithm by using Delphi programming language. Ten thousand random numbers for Pareto distribution have been generated using this program, and the chi-square goodness-of-fit at five percent significance level has been performed.


Related Articles

  • A NOTE ON STOCHASTIC DOMINATION AND CONDITIONAL THINNING. Shah, Sandeep R. // Advances in Applied Probability;Dec2003, Vol. 35 Issue 4, p937 

    This note investigates the simulation algorithm proposed by van Lieshout and van Zwet (2001). It is seen that this algorithm generally produces biased samples; the nature of this bias is further explored in a technical report by the author.

  • Simulation of Algorithms for Performance Analysis. L'Ecuyer, Pierre // INFORMS Journal on Computing;Winter96, Vol. 8 Issue 1, p16 

    Illustrates how to use simulation experiments to understand the behavior of computer algorithms. Levels of modeling for the simulation of algorithms; Information on random number generators (RNG); Recommendations for RNG users who want to avoid bad experiences.

  • An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part II: Observing System Simulation Experiments with Advanced Research WRF (ARW). Chengsi Liu; Qingnong Xiao; Bin Wang // Monthly Weather Review;May2009, Vol. 137 Issue 5, p1687 

    An ensemble-based four-dimensional variational data assimilation (En4DVAR) algorithm and its performance in a low-dimension space with a one-dimensional shallow-water model have been presented in Part I. This algorithm adopts the standard incremental approach and preconditioning in the...

  • A hybrid algorithm for reliability analysis combining Kriging and subset simulation importance sampling. Tong, Cao; Sun, Zhili; Zhao, Qianli; Wang, Qibin; Wang, Shuang // Journal of Mechanical Science & Technology;Aug2015, Vol. 29 Issue 8, p3183 

    To solve the problem of large computation when failure probability with time-consuming numerical model is calculated, we propose an improved active learning reliability method called AK-SSIS based on AK-IS algorithm [1]. First, an improved iterative stopping criterion in active learning is...

  • A new comprehensive study of the 3D random-field Ising model via sampling the density of states in dominant energy subspaces. Fytas, N. G.; Malakis, A. // European Physical Journal B -- Condensed Matter;Mar2006, Vol. 50 Issue 1/2, p39 

    The three-dimensional bimodal random-field Ising model is studied via a new finite temperature numerical approach. The methods of Wang-Landau sampling and broad histogram are implemented in a unified algorithm by using the N-fold version of the Wang-Landau algorithm. The simulations are...

  • Perfect Simulation of Infinite Range Gibbs Measures and Coupling with Their Finite Range Approximations. Galves, A.; Löcherbach, E.; Orlandi, E. // Journal of Statistical Physics;Feb2010, Vol. 138 Issue 1-3, p476 

    In this paper we address the questions of perfectly sampling a Gibbs measure with infinite range interactions and of perfectly sampling the measure together with its finite range approximations. We solve these questions by introducing a perfect simulation algorithm for the measure and for the...

  • Control-variate Models of Common Random Numbers for Multiple Comparisons with the Best. Nelson, Barry L.; Hsu, Jason C. // Management Science;Aug1993, Vol. 39 Issue 8, p989 

    Using common random numbers (CRN) in simulation experiment design is known to reduce the variance of estimators of differences in system performance. However, when more than two systems are compared, exact simultaneous statistical inference in conjunction with CRN is typically impossible. We...

  • Modern Algorithms of Simulation for Getting Some Random Numbers. Anastassiou, G. A.; Iatan, I. F. // Journal of Computational Analysis & Applications;Jan2013, Vol. 15 Issue 1, p1211 

    The date of receipt and acceptance will be inserted by the editor In order to carry out the simulation, we need a source of random numbers distributed according to the desired probability distribution. In this paper we have constructed algorithms for generating both continuous and discrete...

  • Recycling random numbers in the stochastic simulation algorithm. Yates, Christian A.; Klingbeil, Guido // Journal of Chemical Physics;Mar2013, Vol. 138 Issue 9, p094103 

    The stochastic simulation algorithm (SSA) was introduced by Gillespie and in a different form by Kurtz. Since its original formulation there have been several attempts at improving the efficiency and hence the speed of the algorithm. We briefly discuss some of these methods before outlining our...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics