Random linear-quadratic mathematical models: Computing explicit solutions and applications

Authors:
J. C. Cortés;L. Jódar;L. Villafuerte
Affiliations:
Instituto de Matemática Multidisciplinar, Universidad Politécnica de Valencia, Edificio 8G, 2, 46022 Valencia, Spain;Instituto de Matemática Multidisciplinar, Universidad Politécnica de Valencia, Edificio 8G, 2, 46022 Valencia, Spain;Instituto de Matemática Multidisciplinar, Universidad Politécnica de Valencia, Edificio 8G, 2, 46022 Valencia, Spain
Venue:
Mathematics and Computers in Simulation
Year:
2009

Citing 3
Cited 7

Mean square numerical solution of random differential equations: Facts and possibilities

Computers & Mathematics with Applications
Analytic-numerical approximating processes of diffusion equation with data uncertainty

Computers & Mathematics with Applications
Numerical solution of random differential equations: A mean square approach

Mathematical and Computer Modelling: An International Journal

Mean square power series solution of random linear differential equations

Computers & Mathematics with Applications
Random differential operational calculus: Theory and applications

Computers & Mathematics with Applications
Randomness in a mathematical model for the transmission of respiratory syncytial virus (RSV)

Mathematics and Computers in Simulation
Random Airy type differential equations: Mean square exact and numerical solutions

Computers & Mathematics with Applications
Original articles: On the linear advection equation subject to random velocity fields

Mathematics and Computers in Simulation
Numerical solution of random differential models

Mathematical and Computer Modelling: An International Journal
Analytic and numerical solutions of a Riccati differential equation with random coefficients

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals with the study of linear random population models and with a random logistic model (where parameters are random variables). Assuming appropriate conditions, the stochastic processes solutions are obtained under closed form using mean square calculus. Expectation and variance expressions for the stochastic processes solutions are given and illustrative examples are included.