[EN] This paper provides a complete probabilistic description of SIS-type epidemiological models where all the input parameters (contagion rate, recovery rate and initial conditions) are assumed to be random variables. By ...

[EN] In this paper a randomized version of the Beverton-Holt type discrete model is proposed. Its solution stochastic process and the random steady state are determined. Its first probability density function and second ...

This paper proposes a stochastic model to study the evolution of normal and excess weight population between 24 - 65 years old in the region of Valencia (Spain). An approximate solution process of the random model is ...

Consideramos el sistema de ecuaciones lineales, M x = d, con M la matriz de Hilbert de tamaño n y d la suma de la filas de M. El sistema anterior se resolverá usando los algoritmos de Gauss-Seidel y métodos directos. Se ...

[EN] The random variable transformation technique is a powerful method to determine the probabilistic solution for random differential equations represented by the first probability density function of the solution stochastic ...

[EN] In this paper the randomized Cauchy-Euler differential equation is studied. With this aim, from a statistical point of view, both the first and second probability density functions of the solution stochastic process ...

[EN] In this paper mixed diffusion models under decomposable space-time uncertainty are studied. Existence conditions for constructing a mean square convergent series stochastic solution process are given. Methods for ...

Deterministic differential equations are useful tools for mathematical modelling. The consideration of uncertainty into their formulation leads to random differential equations. Solving a random differential equation means ...

A new discretization strategy is introduced for the numerical solution of partial integrodifferential equations appearing in option pricing jump diffusion models. In order to consider the unknown behaviour of the solution ...