Coupling Importance Sampling and Multilevel Monte Carlo using Sample Average Approximation
نویسندگان
چکیده
منابع مشابه
Uniform Convergence of Sample Average Approximation with Adaptive Importance Sampling
We study sample average approximations under adaptive importance sampling. Based on a Banach-space-valued martingale strong law of large numbers, we establish uniform convergence of the sample average approximation to the function being approximated. In the optimization context, we obtain convergence of the optimal value and optimal solutions of the sample average approximation.
متن کاملMultilevel Monte Carlo approximation of functions
Many applications across sciences and technologies require a careful quantification of non-deterministic effects to a system output, for example when evaluating the system’s reliability or when gearing it towards more robust operation conditions. At the heart of these considerations lies an accurate yet efficient characterization of uncertain system outputs. In this work we introduce and analyz...
متن کاملMonte Carlo inference via greedy importance sampling
We present a new method for conducting Monte Carlo inference in graphical models which combines explicit search with generalized importance sampling. The idea is to reduce the variance of importance sampling by searching for significant points in the target distribution. We prove that it is possible to introduce search and still maintain unbiasedness. We then demonstrate our procedure on a few ...
متن کاملModified Monte Carlo with Importance Sampling Method
Monte Carlo simulation methods apply a random sampling and modifications can be made of this method is by using variance reduction techniques (VRT). VRT objective is to reduce the variance due to Monte Carlo methods become more accurate with a variance approaching zero and the number of samples approaches infinity, which is not practical in the real situation (Chen, 2004). These techniques are ...
متن کاملA Critical Note on Empirical (Sample Average, Monte Carlo) Approximation of Solutions to Chance Constrained Programs
The solution of chance constrained optimization problems by means of empirical approximation of the underlying multivariate distribution has recently become a popular alternative to conventional methods due to the efficient application of appropriate mixed integer programming techniques. As the complexity of required computations depends on the sample size used for approximation, exponential es...
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: Methodology and Computing in Applied Probability
سال: 2017
ISSN: 1387-5841,1573-7713
DOI: 10.1007/s11009-017-9579-y