scholarly journals New handy and accurate approximation for the Gaussian integrals with applications to science and engineering

2019 ◽  
Vol 17 (1) ◽  
pp. 1774-1793 ◽  
Author(s):  
Mario A. Sandoval-Hernandez ◽  
Hector Vazquez-Leal ◽  
Uriel Filobello-Nino ◽  
Luis Hernandez-Martinez

Abstract In this work, we propose to approximate the Gaussian integral, the error function and the cumulative distribution function by using the power series extender method (PSEM). The approximations proposed in this paper present a high accuracy for the complete domain [–∞,∞]. Furthermore, the approximations are handy and easy computable, avoiding the application of special numerical algorithms. In order to show its high accuracy, three case studies are presented with applications to science and engineering.

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Thomas L. Toulias ◽  
Christos P. Kitsos

This paper introduces, investigates, and discusses the -order generalized lognormal distribution (-GLD). Under certain values of the extra shape parameter , the usual lognormal, log-Laplace, and log-uniform distribution, are obtained, as well as the degenerate Dirac distribution. The shape of all the members of the -GLD family is extensively discussed. The cumulative distribution function is evaluated through the generalized error function, while series expansion forms are derived. Moreover, the moments for the -GLD are also studied.


2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Hector Vazquez-Leal ◽  
Roberto Castaneda-Sheissa ◽  
Uriel Filobello-Nino ◽  
Arturo Sarmiento-Reyes ◽  
Jesus Sanchez Orea

The integral of the standard normal distribution function is an integral without solution and represents the probability that an aleatory variable normally distributed has values between zero and . The normal distribution integral is used in several areas of science. Thus, this work provides an approximate solution to the Gaussian distribution integral by using the homotopy perturbation method (HPM). After solving the Gaussian integral by HPM, the result served as base to solve other integrals like error function and the cumulative distribution function. The error function is compared against other reported approximations showing advantages like less relative error or less mathematical complexity. Besides, some integrals related to the normal (Gaussian) distribution integral were solved showing a relative error quite small. Also, the utility for the proposed approximations is verified applying them to a couple of heat flow examples. Last, a brief discussion is presented about the way an electronic circuit could be created to implement the approximate error function.


Author(s):  
RONALD R. YAGER

We look at the issue of obtaining a variance like measure associated with probability distributions over ordinal sets. We call these dissonance measures. We specify some general properties desired in these dissonance measures. The centrality of the cumulative distribution function in formulating the concept of dissonance is pointed out. We introduce some specific examples of measures of dissonance.


2017 ◽  
Vol 20 (5) ◽  
pp. 939-951
Author(s):  
Amal Almarwani ◽  
Bashair Aljohani ◽  
Rasha Almutairi ◽  
Nada Albalawi ◽  
Alya O. Al Mutairi

Sign in / Sign up

Export Citation Format

Share Document