منابع مشابه
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...
متن کاملConsensus Convergence with Stochastic Effects
We consider a stochastic, continuous state and time opinion model where each agent’s opinion locally interacts with other agents’ opinions in the system, and there is also exogenous randomness. The interaction tends to create clusters of common opinion. By using linear stability analysis of the associated nonlinear Fokker-Planck equation that governs the empirical density of opinions in the lim...
متن کاملOn Convergence of Stochastic Processes
where £iX) is the distribution function of the random variable X,f( ) is a real-valued function 5 continuous almost everywhere (p), and the limit is in the sense of the usual weak convergence of distributions. Equation (2) is usually the real center of interest, for many " limit-distribution theorems" are implicit in it. It is clear that for given {pn} and p, the better theorem of this kind wou...
متن کاملConvergence rates in monotone separable stochastic networks
We study bounds on the rate of convergence to the stationary distribution in monotone separable networks which are represented in terms of stochastic recursive sequences. Monotonicity properties of this subclass of Markov chains allow us to formulate conditions in terms of marginal network characteristics. Two particular examples, generalized Jackson networks and multiserver queues, are conside...
متن کاملNeural Computation . On the Convergence of Stochastic
Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD( ) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of conve...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Advances in Mathematics
سال: 1976
ISSN: 0001-8708
DOI: 10.1016/0001-8708(76)90106-7