Cortés, J.-C.; El-Labany, S.K.; Navarro-Quiles, A.; Selim, Mustafa M.; Slama, H.(John Wiley & Sons, 2020-09-30)
[EN] This paper provides a comprehensive probabilistic analysis of a full randomization of approximate SIR-type epidemiological models based on discrete-time Markov chain formulation. The randomization is performed by ...
Casabán, M.C.; Cortés, J.-C.; Navarro-Quiles, A.; Romero, José-Vicente; Roselló, María-Dolores; Villanueva Micó, Rafael Jacinto(Elsevier, 2016)
[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] We randomize the following class of linear differential equations with delay, x(tau)' (t) = ax(tau) (t) bx(tau) (t -tau), t> 0, and initial condition, x(tau )(t) = g(t), -tau <= t <= 0, by assuming that coefficients ...
[EN] In this paper, we deal with computational uncertainty quantification for stochastic models with one random input parameter. The goal of the paper is twofold: First, to approximate the set of probability density functions ...
Casabán, M.-C.; Cortés, J.-C.; Navarro-Quiles, A.; Romero, José-Vicente; Roselló, María-Dolores; Villanueva Micó, Rafael Jacinto(Elsevier, 2017)
[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 ...
Cortés, J.-C.; Romero, José-Vicente; Roselló, María-Dolores; Villanueva Micó, Rafael Jacinto(Elsevier, 2017)
[EN] Generalized polynomial chaos (gPC) is a spectral technique in random space to represent random variables and stochastic processes in terms of orthogonal polynomials of the Askey scheme. One of its most fruitful ...
Calatayud, Julia; Caraballo, Tomás; Cortés, J.-C.; Jornet, Marc(Texas State University. Department of Mathematics, 2020-05-26)
[EN] In this article we analyze the randomized non-autonomous Bertalanffy model
x' (t, omega) = a(t, omega)x(t, omega) b(t, omega)x(t, omega)(2/3), x(t(0), omega) = x(0)(omega),
where a(t, omega) and b(t, omega) are ...
Casabán, M. C.; Cortés, J.C.; Navarro-Quiles, A.; Romero, José-Vicente; Roselló, María-Dolores; Villanueva Micó, Rafael Jacinto(Elsevier, 2016)
[EN] This paper deals with the determination of the first probability density function of the solution stochastic process to the homogeneous Riccati differential equation taking advantage of both linearization and Random ...
[EN] Classical Markov models are defined through a stochastic transition matrix, i.e., a matrix whose columns (or rows) are deterministic values representing transition probabilities. However, in practice these quantities ...
[EN] This paper deals with the approximate computation of the first probability density function of the solution stochastic process to second-order linear differential equations with random analytic coefficients about ...
[EN] In this contribution, we construct approximations for the density associated with the solution of second-order linear differential equations whose coefficients are analytic stochastic processes about regular-singular ...
[EN] The study of the dynamics of the size of a population via mathematical modelling is a problem of interest and widely studied. Traditionally, continuous deterministic methods based on differential equations have been ...
[EN] This paper is addressed to give a generalization of the classical Markov methodology allowing the treatment of the entries of the transition matrix and initial condition as random variables instead of deterministic ...