Three sampling methods are compared for efficiency on a number of test problems of various complexity for which analytic quadratures are available. The methods compared are Monte Carlo with pseudo-random and Latin Hypercube Sampling and the Quasi Monte Carlo method with sampling based on Sobol’ sequences. Generally, results show superior performance of the Quasi Monte Carlo approach based on Sobol’ sequences in line with theoretical predictions. There are also some types of functions for which Latin Hypercube Sampling can be more efficient than the Monte Carlo method. For the same functions types it can be more efficient than the Quasi Monte Carlo method at small number of sampled points.JRC.DDG.01-Econometrics and applied statistic
The semi-Bayesian approach for constructing efficient stated choice designs requires the evaluation ...
In this chapter, we present a general introduction to Monte Carlo (MC)-based methods, sampling metho...
The present section will focus on the applicability issues of Monte Carlo-based methods, as well as ...
Monte Carlo analysis has become nearly ubiquitous since its introduction, now over 65 years ago. It ...
In recent years, new, intelligent and efficient sampling techniques for Monte Carlo simulation have ...
We compare the convergence properties of two different quasi-random sampling designs – Sobol’s quasi...
The Latin Hypercube Sampling, LHS, plan was presented by McKay, Beckman and Conover (Technometrics, ...
International audienceWe analyze an extended form of Latin hypercube sampling technique that can be ...
We study the performance of alternative sampling methods for estimating multivariate normal probabil...
textabstractWe study the performance of alternative sampling methods for estimating multivariate nor...
We look at the benefits of using a kind of quasi-random numbers to obtain more accurate results for ...
Monte Carlo (MC) techniques are commonly used to perform uncertainty and sensitivity analyses. A key...
Many control problems are so complex that analytic techniques fail to solve them [2]. Furthermore, e...
Latin hypercube sampling is suggested as a tool to improve the efficiency of different importance sa...
McKay, Conover and Beckman (1979) introduced Latin hypercube sampling (LHS) for reducing variance of...
The semi-Bayesian approach for constructing efficient stated choice designs requires the evaluation ...
In this chapter, we present a general introduction to Monte Carlo (MC)-based methods, sampling metho...
The present section will focus on the applicability issues of Monte Carlo-based methods, as well as ...
Monte Carlo analysis has become nearly ubiquitous since its introduction, now over 65 years ago. It ...
In recent years, new, intelligent and efficient sampling techniques for Monte Carlo simulation have ...
We compare the convergence properties of two different quasi-random sampling designs – Sobol’s quasi...
The Latin Hypercube Sampling, LHS, plan was presented by McKay, Beckman and Conover (Technometrics, ...
International audienceWe analyze an extended form of Latin hypercube sampling technique that can be ...
We study the performance of alternative sampling methods for estimating multivariate normal probabil...
textabstractWe study the performance of alternative sampling methods for estimating multivariate nor...
We look at the benefits of using a kind of quasi-random numbers to obtain more accurate results for ...
Monte Carlo (MC) techniques are commonly used to perform uncertainty and sensitivity analyses. A key...
Many control problems are so complex that analytic techniques fail to solve them [2]. Furthermore, e...
Latin hypercube sampling is suggested as a tool to improve the efficiency of different importance sa...
McKay, Conover and Beckman (1979) introduced Latin hypercube sampling (LHS) for reducing variance of...
The semi-Bayesian approach for constructing efficient stated choice designs requires the evaluation ...
In this chapter, we present a general introduction to Monte Carlo (MC)-based methods, sampling metho...
The present section will focus on the applicability issues of Monte Carlo-based methods, as well as ...