Solving Random Homogeneous Linear Second-Order Differential Equations: A Full Probabilistic Description
2016
In this paper a complete probabilistic description for the solution of random homogeneous linear second-order differential equations via the computation of its two first probability density functions is given. As a consequence, all unidimensional and two-dimensional statistical moments can be straightforwardly determined, in particular, mean, variance and covariance functions, as well as the first-order conditional law. With the aim of providing more generality, in a first step, all involved input parameters are assumed to be statistically dependent random variables having an arbitrary joint probability density function. Second, the particular case that just initial conditions are random variables is also analysed. Both problems have common and distinctive feature which are highlighted in our analysis. The study is based on random variable transformation method. As a consequence of our study, the well-known deterministic results are nicely generalized. Several illustrative examples are included.
Keywords:
- Random function
- Random element
- Multivariate random variable
- Algebra of random variables
- Mathematical analysis
- Algebraic formula for the variance
- Covariance mapping
- Convergence of random variables
- Moment-generating function
- Mathematics
- Sum of normally distributed random variables
- Conditional probability distribution
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