Let (Ω,,P) be a probability space and let τ: ℝ × Ω → ℝ be strictly increasing and continuous with respect to the first variable, and -measurable with respect to the second variable. We obtain a partial characterization and a uniqueness-type result for solutions of the general linear equation $F\left(x\right)={\int }_{\Omega }F\left(\tau (x,\omega )\right)P\left(d\omega \right)$ in the class of probability distribution functions.

It has been proved recently that the two-direction refinement equation of the form $f\left(x\right)={\sum }_{n\in }{c}_{n,1}f(kx-n)+{\sum }_{n\in \mathbb{Z}}{c}_{n,-1}f(-kx-n)$ can be used in wavelet theory for constructing two-direction wavelets, biorthogonal wavelets, wavelet packages, wavelet frames and others. The two-direction refinement equation generalizes the classical refinement equation $f\left(x\right)={\sum }_{n\in \mathbb{Z}}cₙf(kx-n)$, which has been used in many areas of mathematics with important applications. The following continuous extension of the classical refinement equation $f\left(x\right)={\int }_{ℝ}c\left(y\right)f(kx-y)dy$ has also various interesting applications....

In this paper we study a linear integral equation $x\left(t\right)=a\left(t\right)-{\int }_0^{t}C(t,s)x\left(s\right)\mathrm{d}s$, its resolvent equation $R(t,s)=C(t,s)-{\int }_s^{t}C(t,u)R(u,s)\mathrm{d}u$, the variation of parameters formula $x\left(t\right)=a\left(t\right)-{\int }_0^{t}R(t,s)a\left(s\right)\mathrm{d}s$, and a perturbed equation. The kernel, $C(t,s)$, satisfies classical smoothness and sign conditions assumed in many real-world problems. We study the effects of perturbations of $C$ and also the limit sets of the resolvent. These results lead us to the study of nonlinear perturbations.

Let (Ω,,P) be a probability space and let τ: ℝ×Ω → ℝ be a function which is strictly increasing and continuous with respect to the first variable, measurable with respect to the second variable. Given the set of all continuous probability distribution solutions of the equation $F\left(x\right)={\int }_{\Omega }F\left(\tau (x,\omega )\right)dP\left(\omega \right)$ we determine the set of all its probability distribution solutions.