In this work we study optimization problems subject to a failure constraint. This constraint is expressed in terms of a condition that causes failure, representing a physical or technical breakdown. We formulate the problem in terms of a probability constraint, where the level of “confidence” is a modelling parameter and has the interpretation that the probability of failure should not exceed t...

We generalize the convergenece of an optimal semimeasure to a real probability in algorithmic probability by using game-theoretic probability theory and the theory of computable topology. Two lemmas in the proof give as corollary the existence of an optimal test and an optimal integral test, which are important from the point of view of algorithmic randomness. We only consider an SCT3 space, wh...

In this paper, a concept of degree of population diversity is introduced to quantitatively characterize and theoretically analyze the problem of premature convergence in genetic algorithms (GAs) within the framework of Markov chain. Under the assumption that the mutation probability is zero, the search ability of GA is discussed. It is proved that the degree of population diversity converges to...

in present study, role of agricultural on eco countries regional convergence was assessed in dynamic panel data model. at first, eco countries convergence and agricultural sector effect in convergence acceleration was surveyed by generalized method of moments (gmm) over the period 1996-2005. results showed that, this is convergence between eco countries. in spite of increasing regional growth, ...

In a Bayesian network, a convergence error may arise in the probabilities computed for a node with two or more parents in the same loop (in this paper such a node is called a convergence node), when in the computation of these probabilities the information of the parents is processed as if they are independent. In this paper the range of the convergence error for the basic case of a loop with j...

The classical Lévy-Cramér continuity theorem asserts that the convergence of the characteristic functions implies the weak convergence of the corresponding probability measures. We extend this result to the setting of non-commutative probability theory and discuss some applications. ∗CNRS, Université de Provence, Université de la Méditerranée, Université du Sud Toulon-Var. 2 V. Jakšić, Y. Pautr...

Convergence of the ensemble Kalman filter in the limit for large ensembles to the Kalman filter is proved. In each step of the filter, convergence of the ensemble sample covariance follows from a weak law of large numbers for exchangeable random variables, the continuous mapping theorem gives convergence in probability of the ensemble members, and Lp bounds on the ensemble then give Lp converge...

A new approach to the planning and analysis of the successful convergence of assembly is presented. The convergence is refered to as p-convergence, since it is derived within a probability framework. The assembly is modeled as a hybrid dynamic system (HDS) accounting for the presence of the discrete change of contact and the continuous movement during the process. The continuous property is the...