Some Convergence Theorems for Stationary Stochastic Processes

Stochastic Processes ( Fall 2014 ) Spectral representations and ergodic theorems for stationary stochastic processes Stationary stochastic processes

A stochastic process X is strongly stationary if its fdds are invariant under time shifts, that is, for any (finite) n, for any t0 and for all t1, ..., tn ∈ T , (Xt1 , ..., Xtn) and (Xt1+t0 , ..., Xtn+t0) have the same distribution. A stochastic process X is weakly stationary if its mean function is constant and its covariance function is invariant under time shifts. That is, for all t ∈ T , E(...

Some Inference Theorems in Stochastic Processes

On the Rate of Convergence of Some Stochastic Processes

We present a general technique for obtaining bounds on the deviation of the optimal value of some stochastic combinatorial problems from their mean. As a particular application, we prove an exponential rate of convergence for the length of a shortest path through n random points in the unit square. This strengthens a previous result of Steele [St (1981b)].

ON CONVERGENCE THEOREMS FOR FUZZY HENSTOCK INTEGRALS

The main purpose of this paper is to establish different types of convergence theorems for fuzzy Henstock integrable functions, introduced by  Wu and Gong cite{wu:hiff}. In fact, we have proved fuzzy uniform convergence theorem, convergence theorem for fuzzy uniform Henstock integrable functions and fuzzy monotone convergence theorem. Finally, a necessary and sufficient condition under which th...

Convergence of Stochastic Processes

Often the best way to adumbrate a dark and dense assemblage of material is to describe the background in contrast to which the edges of the nebulosity may be clearly discerned. Hence, perhaps the most appropriate way to introduce this paper is to describe what it is not. It is not a comprehensive study of stochastic processes, nor an in-depth treatment of convergence. In fact, on the surface, t...