Multivariate Normal Approximation for the Stochastic Simulation Algorithm: Limit Theorem and Applications
نویسندگان
چکیده
منابع مشابه
Multivariate Stochastic Simulation with Subjective Multivariate Normal Distributions1
-In many applications of Monte Carlo simulation in forestry or forest products, it may be known that some variables are correlated. However, for simplicity, in most simulations it has been assumed that random variables are independently distributed. This report describes an alternative Monte Carlo simulation technique for subjectively assessed multivariate normal distributions. The method requi...
متن کاملThe central limit theorem and Poisson approximation
2 Poisson approximation 6 2.1 A coupling approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Stein’s method for Poisson approximation . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Independent summands . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.2 Dependent summands: the local approach . . . . . . . . . . . . . . . . . 10 2.2.3 Size biasing and coup...
متن کاملthe algorithm for solving the inverse numerical range problem
برد عددی ماتریس مربعی a را با w(a) نشان داده و به این صورت تعریف می کنیم w(a)={x8ax:x ?s1} ، که در آن s1 گوی واحد است. در سال 2009، راسل کاردن مساله برد عددی معکوس را به این صورت مطرح کرده است : برای نقطه z?w(a)، بردار x?s1 را به گونه ای می یابیم که z=x*ax، در این پایان نامه ، الگوریتمی برای حل مساله برد عددی معکوس ارانه می دهیم.
15 صفحه اولA Discrete Parameter Stochastic Approximation Algorithm for Simulation Optimization
We develop in this paper a two-timescale simultaneous perturbation stochastic approximation algorithm for simulation based parameter optimization over discrete sets. This algorithm is applicable in cases where the cost to be optimized is in itself the longrun average of certain cost functions whose noisy estimates are obtained via simulation. We present the convergence analysis of our algorithm...
متن کاملA Limit Theorem for Stochastic Acceleration
We consider the motion of a particle in a weak mean zero random force field F, which depends on the position, x(t), and the velocity, v(t) = 2(0. The equation of motion is 2(0 = ef(x( t ) , v(t), 0~), where x( ') and v(-) take values in R d, d > 3, and co ranges over some probability space. We show, under suitable mixing and moment conditions on F, that as e--+ 0, v~( t ) v(t/e 2) converges wea...
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 Notes in Theoretical Computer Science
سال: 2015
ISSN: 1571-0661
DOI: 10.1016/j.entcs.2015.06.011