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

Author

Listed:
  • Cortés, J.C.
  • Jódar, L.
  • Villafuerte, L.

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.

Suggested Citation

  • Cortés, J.C. & Jódar, L. & Villafuerte, L., 2009. "Random linear-quadratic mathematical models: Computing explicit solutions and applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(7), pages 2076-2090.
    • Handle: RePEc:eee:matcom:v:79:y:2009:i:7:p:2076-2090
    DOI: 10.1016/j.matcom.2008.11.008
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    Cited by:

    1. Bevia, V. & Burgos, C. & Cortés, J.-C. & Navarro-Quiles, A. & Villanueva, R.-J., 2020. "Uncertainty quantification analysis of the biological Gompertz model subject to random fluctuations in all its parameters," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    2. Dorini, F.A. & Cunha, M.C.C., 2011. "On the linear advection equation subject to random velocity fields," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 82(4), pages 679-690.
    3. Arenas, Abraham J. & González-Parra, Gilberto & Jódar, Lucas, 2010. "Randomness in a mathematical model for the transmission of respiratory syncytial virus (RSV)," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(5), pages 971-981.
    4. Cortés, J.-C. & Moscardó-García, A. & Villanueva, R.-J., 2022. "Uncertainty quantification for hybrid random logistic models with harvesting via density functions," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

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