Concentration bounds for stochastic approximations
نویسندگان
چکیده
منابع مشابه
Bounds and Approximations for Multistage Stochastic Programs
Consider (typically large) multistage stochastic programs, which are defined on scenario trees as the basic data structure. It is well known that the computational complexity of the solution depends on the size of the tree, which itself increases typically exponentially fast with its height, i.e. the number of decision stages. For this reason approximations which replace the problem by a simple...
متن کاملMinimax Bounds for Autoregressive Approximations
The subject of this paper is autoregressive AR approximations of a stationary Gaus sian discrete time process based on a nite sequence of observations We adopt the non parametric minimax framework and study how well can the process be approximated by a nite order autoregressive model Our results show that a properly chosen model dimen sion leads to an optimal in order minimax estimator
متن کاملApproximations for Stochastic Graph Rewriting
In this note we present a method to compute approximate descriptions of a class of stochastic systems. For the method to apply, the system must be presented as a Markov chain on a state space consisting in graphs or graph-like objects, and jumps must be described by transformations which follow a finite set of local rules. The method is a form of static analysis and uses a technique which is re...
متن کاملUpper Error Bounds for Approximations of Stochastic Differential Equations with Markovian Switching
We consider stochastic differential equations with Markovian switching (SDEwMS). An SDEwMS is an ordinary stochastic differential equation with drift and diffusion coefficients depending not only on the current state of the solution but also on the current state of a right-continuous Markov chain taking values in a finite state space. Let W be a one-dimensional Brownian motion on the unit inter...
متن کاملExplicit cost bounds of stochastic Galerkin approximations for parameterized PDEs with random coefficients
This work analyzes the overall computational complexity of the stochastic Galerkin finite element method (SGFEM) for approximating the solution of parameterized elliptic partial differential equations with both affine and non-affine random coefficients. To compute the fully discrete solution, such approaches employ a Galerkin projection in both the deterministic and stochastic domains, produced...
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: Electronic Communications in Probability
سال: 2012
ISSN: 1083-589X
DOI: 10.1214/ecp.v17-1952