A family of constrained diffusions in random environment is considered. Constraint set is a polyhedral cone and coefficients of the diffusion are governed by, in addition to the system state, a finite state Markov process that is independent of the driving noise. Such models arise as limit objects in the heavy traffic analysis of stochastic processing networks (SPN) with Markov modulated arriva...

This paper is devoted to the question of whether traders can learn rational expectations from repeated observations of market data in a stationary environment with finitely many exogenous states of the world. The learning problem is placed in the context of an iterative adjustment process which achieves equilibrium if traders have rational expectations. The main result is that even if traders b...

Generation of a stationary environmental map i s one of the important tasks for vision based robot navigation. Under the assumption of known motion of a robot, environmental maps of a real scene can be successfilly generated by monitoring azimuth changes in a n image. Several researchers have used this property for robot navagation However, it i s difficult to observe the exact motion parameter...

In distributed classification, each learner observes its environment and deduces a classifier. As a learner has only a local view of its environment, classifiers can be exchanged among the learners and integrated, or merged, to improve accuracy. However, the operation of merging is not defined for most classifiers. Furthermore, the classifiers that have to be merged may be of different types in...

A fundamental challenge for the visual system is to extract the 3D spatial structure of the environment. When an observer translates without moving the eyes, the retinal speed of a stationary object is related to its distance by a scale factor that depends on the velocity of the observer's self-motion. Here, we aim to test whether the brain uses vestibular cues to self-motion to estimate distan...

The selection of an equilibrium state by maximizing the entropy of a system, subject to certain constraints, is often powerfully motivated as an exercise in logical inference, a procedure where conclusions are reached on the basis of incomplete information. But such a framework can be more compelling if it is underpinned by dynamical arguments, and we show how this can be provided by stochastic...

In this paper, we propose a minimum-error entropy with self-adjusting step -size (MEE-SAS) as an alternative to the minimum-error entropy (MEE) algorithm for training adaptive systems. MEE-SAS has faster speed of convergence as compared to MEE algorithm for the same misadjustment. We attribute the self-adjusting step-size property of MEE-SAS to its changing curvature as opposed to MEE which has...

Environments that vary over time present a fundamental problem to adaptive systems. Although in the worst case there is no hope of eeective adaptation, some forms environmental variability do provide adaptive opportunities. We consider a broad class of non-stationary environments, those which combine a variable result function with an invariant utility function, and demonstrate via simulation t...

The environment in which a population evolves can have a crucial impact on selection. We study evolutionary dynamics in finite populations of fixed size in a changing environment. The population dynamics are driven by birth and death events. The rates of these events may vary in time depending on the state of the environment, which follows an independent Markov process. We develop a general the...

We present a microscopic model of a charge carrier transfer under an action of a constant electric field in a complex medium. Generalizing previous theoretical approaches, we model the dynamical environment hindering the carrier motion by dynamic perco-lation, i.e., as a medium comprising particles which move randomly on a simple cubic lattice, constrained by hard-core exclusion, and may sponta...