As the feature size of integrated circuits scales to ever smaller regimes, maintaining the paradigm of deterministic Boolean computation is increasingly challenging. Indeed, mounting concerns over noise and uncertainty in signal values motivate a new approach: the design of stochastic logic, that is to say, digital circuitry that processes signals probabilistically, and so can cope with errors ...

To improve routing security in MPLS network, base on the stochastic routing algorithm, we propose a proactive mechanism we call enhanced secure heuristic-stochastic routing (ESHSR), which brings to bear Bayesian principle, explores the existence of multiple routes and forces packets to take alternate paths probabilistically. In this paper, we investigate game theoretic techniques to develop rou...

Probability distributions are central tools for probabilistic modeling in data mining. In functional data analysis (FDA) they are weakly studied in the general case. In this paper we discuss a probability distribution law for functional data considered as stochastic process. We define first a new kind of stationarity linked t o the Archimedean copulas, and then we build a probability distributi...

The CESTACmethod and its implementation known as CADNA software have been created to estimate the accuracy of the solution of real life problems when these solutions are obtained from numerical methods implemented on a computer. The method takes into account uncertainties on data and round-off errors. On another hand a theoretical model for this method in which operands are gaussian variables c...

Varied sensory systems use noise in order to enhance detection of weak signals. It has been conjectured in the literature that this effect, known as stochastic resonance, may take place in central cognitive processes such as memory retrieval of arithmetical multiplication. We show, in a simplified model of cortical tissue, that complex arithmetical calculations can be carried out and are enhanc...

This paper presents a stochastic computing implementation of a Bayesian sensorimotor system that performs obstacle avoidance for an autonomous robot. In a previous work we have shown that we are able to automatically design a probabilistic machine which computes inferences on a Bayesian model using stochastic arithmetic. We start from a high level Bayesian model description, then our compiler g...

The dynamics of a system formed by a finite number N of globally coupled bistable oscillators and driven by external forces is studied focusing on a global variable defined as the arithmetic mean of each oscillator variable. Several models based on truncation schemes of a hierarchy of stochastic equations for a set of fluctuating cumulant variables are presented. This hierarchy is derived using...

We study the weak convergence of iterates of so–called centred kernel quadratic stochastic operators. These iterations, in a population evolution setting, describe the additive perturbation of the arithmetic mean of the traits of an individual’s parents and correspond to certain weighted sums of independent random variables. We show that one can obtain weak convergence results under rather mild...

Most digital circuits process information that is encoded as zeros and ones determin-istically. For example, the arithmetic unit of a modern computer performs calculationson deterministic integer or floating-point values represented in binary radix. However,digital computation need not be deterministic. In this dissertation, we consider an al-ternative paradigm: digital circuits...

In this paper we present how to perform the pricing of American-style options in practice with use of the stochastic mesh method. We show various types of the estimators. Then we indicate their properties on the examples. We determine the values of parameters which allow to implement a method efficiently. Finally, we present how to use the method on a challenging problem of pricing arithmetic a...