The First Passage Time and Estimation of the Number of Level-Crossings for a Telegraph Process

It is a well-known result that almost all sample paths of a Brownian motion or Wiener process {W(t)} have infinitely many zero-crossings in the interval (0, δ) for δ > 0. Under the Kac condition, the telegraph process weakly converges to the Wiener process. We estimate the number of intersections of a level or the number of level-crossings for the telegraph process. Passing to the limit under the Kac condition, we also obtain an estimate of the level-crossings for the Wiener process.Відомо, що майже всі ви6іркові траєкторії броунівського руху чи вінєрівського процесу {W(t) мають нескінченно багато нульових перетинів в інтервалі (0, δ) при δ > 0. За умови Каца телеграфний процес слабко збігається до вінерівського процесу. В роботі оцінюється число перетинів рівня для телеграфного процесу. Переходячи до границі за умови Каца, ми також отримуємо оцінку перетинів рівня для вінерівського процесу

Solutions of some partial differential equations with variable coefficients by properties of monogenic functions

In this paper we study some partial differential equations by using properties of Gateaux differentiable functions on commutative algebra. It is proved that components of differentiable functions satisfy some partial differential equations with coefficients related with properties of bases of subspaces of the corresponding algebra

On random flights with non-uniformly distributed directions

This paper deals with a new class of random flights $\underline{\bf X}_d(t),t>0,$ defined in the real space $\mathbb{R}^d, d\geq 2,$ characterized by non-uniform probability distributions on the multidimensional sphere. These random motions differ from similar models appeared in literature which take directions according to the uniform law. The family of angular probability distributions introduced in this paper depends on a parameter $\nu\geq 0$ which gives the level of drift of the motion. Furthermore, we assume that the number of changes of direction performed by the random flight is fixed. The time lengths between two consecutive changes of orientation have joint probability distribution given by a Dirichlet density function. The analysis of $\underline{\bf X}_d(t),t>0,$ is not an easy task, because it involves the calculation of integrals which are not always solvable. Therefore, we analyze the random flight $\underline{\bf X}_m^d(t),t>0,$ obtained as projection onto the lower spaces $\mathbb{R}^m,m<d,$ of the original random motion in $\mathbb{R}^d$. Then we get the probability distribution of $\underline{\bf X}_m^d(t),t>0.$ Although, in its general framework, the analysis of $\underline{\bf X}_d(t),t>0,$ is very complicated, for some values of $\nu$, we can provide some results on the process. Indeed, for $\nu=1$, we obtain the characteristic function of the random flight moving in $\mathbb{R}^d$. Furthermore, by inverting the characteristic function, we are able to give the analytic form (up to some constants) of the probability distribution of $\underline{\bf X}_d(t),t>0.$Comment: 28 pages, 3 figure