2 Diffusive limit for a particle in a random flow 7 2.1 Diffusion of a particle in a time-dependent random flow . . . . . . . . . . . . . . . . 7 2.1.1 The central limit theorem, purely time-dependent flows, and diffusion . . . . 8 2.1.2 Using formal asymptotic expansions . . . . . . . . . . . . . . . . . . . . . . . 9 2.1.3 Random flows with spatial-temporal dependence . . . . . . . . . . . . ...

a total of 2519 blood samples were collected from five distinct populations of larestan, in fars province, in iran (lar, gerash, khour, latifi and berake) were examined for abo and rh blood groups. the gene frequencies obtained from these samples were compared with the hitherto reported corresponding data from other populations of fars province to determine the genetic structure of the larestan...

1.1. Random walk in random potential. Let S = (S(n))n∈N0 be a nearestneighbor random walk on the lattice Zd, with start at the origin and drift h into the direction of the first axis. We suppose S being defined on a probability space (Ω,F , Ph), and we denote by Eh the associated expectation. Such a random process is characterized by the distributions of its finite-step sub-paths S[n] def = S(0...

1.1. Random walk in random potential. Let S = (S(n))n∈N0 be a nearestneighbor random walk on the lattice Zd, with start at the origin and drift h into the direction of the first axis. We suppose S being defined on a probability space (Ω,F , Ph), and we denote by Eh the associated expectation. Such a random process is characterized by the distributions of its finite-step sub-paths S[n] def = S(0...

Stochastic differential equations (SDEs) are useful to model continuous stochastic processes. When (independent) repeated temporal data are available, variability between the trajectories can be modeled by introducing random effects in the drift of the SDEs. These models are useful to analyse neuronal data, crack length data, pharmacokinetics, financial data, to cite some applications among oth...

Random walks are used to model movement in a wide variety of contexts: from the movement of cells undergoing chemotaxis to the migration of animals. In a two-dimensional biased random walk, the diffusion about the mean drift position is entirely dependent on the moments of the angular distribution used to determine the movement direction at each step. Here we consider biased random walks using ...

Stochastic differential equations (SDEs) are useful to model continuous stochastic processes. When (independent) repeated temporal data are available, variability between the trajectories can be modeled by introducing random effects in the drift of the SDEs. These models are useful to analyse neuronal data, crack length data, pharmacokinetics, financial data, to cite some applications among oth...

The dynamics of a classical particle in a one-dimensional, randomly driven potential is analysed both analytically and numerically. The potential considered here is composed of two identical spatially periodic saw-tooth-like components, one of which is externally driven by a random force. We show that under certain conditions the particle may travel against the averaged external force, performi...

It is often assumed that learning takes place by changing an otherwise stable neural representation. To test this assumption, we studied changes in the directional tuning of primate motor cortical neurons during reaching movements performed in familiar and novel environments. During the familiar task, tuning curves exhibited slow random drift. During learning of the novel task, random drift was...

We consider N independent stochastic processes (Xi(t), t ∈ [0, Ti]), i = 1, . . . , N , defined by a stochastic differential equation with drift term depending on a random variable φi. The distribution of the random effect φi depends on unknown parameters which are to be estimated from the continuous observation of the processes Xi. We give the expression of the exact likelihood. When the drift...