a continuous approximation fitting to the discrete distributions using ode

A continuous approximation fitting to the discrete distributions using ODE

The probability density functions fitting to the discrete probability functions has always been needed, and very important. This paper is fitting the continuous curves which are probability density functions to the binomial probability functions, negative binomial geometrics, poisson and hypergeometric. The main key in these fittings is the use of the derivative concept and common differential ...

Continuous Discrete Variable Optimization of Structures Using Approximation Methods

Optimum design of structures is achieved while the design variables are continuous and discrete. To reduce the computational work involved in the optimization process, all the functions that are expensive to evaluate, are approximated. To approximate these functions, a semi quadratic function is employed. Only the diagonal terms of the Hessian matrix are used and these elements are estimated fr...

Reexamining Discrete Approximations to Continuous Distributions

D is a common decision analysis technique for which many methods are described in the literature and employed in practice. The accuracy of these methods is typically judged by how well they match the mean, variance, and possibly higher moments of the underlying continuous probability distribution. Previous authors have analyzed the accuracy of differing discretization methods across a limited s...

A Method to Expand Family of Continuous Distributions based on Truncated Distributions

 Abstract: A new method to generate various family of distributions is introduced. This method introduces a new two-parameter extension of the exponential distribution to illustrate its application. Some statistical and reliability properties of the new distribution, including explicit expressions for the moments, quantiles, mode, moment generating function, mean residual lifetime, stochas...

A weak approximation for the Extrema's distributions of Levy processes

‎Suppose that $X_{t}$ is a one-dimensional and real-valued L'evy‎ ‎process started from $X_0=0$‎, ‎which ({bf 1}) its nonnegative‎ ‎jumps measure $nu$ satisfying $int_{Bbb‎ ‎R}min{1,x^2}nu(dx)

Probability Distributions: Discrete vs. Continuous