MONTE CORLO MOTHED ON PROBABILITY FUNCTION AND DOUBLE INTEGRALS

Abstract

The Monte Carlo simulation is a technique and can be used to numerically represent a physical problem based on the deterministic model. This is achievable by utilizing random numbers generated on the basis of probable distribution of parameters as inputs. A central role of statisticians’ is to assess and quantity uncertainty associated with estimation or inference based on a finite sample. The idea of Monte Carlo, random numbers, probability density function, probability distribution, discrete probability distribution, continuous probability distribution and double integrals by Monte Carlo method with Simpson method discussed in this study . The researcher argue that where the randomness comes from and to evaluate double integrals. An important part of the analysis of numerical integration method has been studied the behavior of the approximation error as a function of the number of integrand evaluations and iterated double integrals of Simpson’s method approximation. In Monte Carlo method n y y y ,..., , 2 1 are independent and identically distributed with the same distribution as y . ݌ ௥ (lim ௡→ஶ |ߤ ௡ − ߤ| = ߤ) = 1 , to assume that ߤ exists. Once Monte Carlo idea fails; if ߤ did not exist. In this paper when the researcher comparable Monte Carlo method and Simpson method, The numerical method of solving double integral by Simpson method for variable limit is better approximation than Monte Carlo method.