A Random-Discretization Based Monte Carlo Sampling Method and its Applications

By James C. Fu, Liqun Wang. 2002

59 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9c3e15b03aa348e2d7cd4bac3859b7904514c41b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Liqun Wang
2. S. Shan
3. G. G. Wang

In Statistics and Probability

1. S. Shan
2. G. G. Wang
3. Liqun Wang
4. Amir H. Gandomi
5. Géraud Blatman
6. Bruno Sudret
7. Yew-Soon Ong
8. Jianfei Cai

Citations in the corpus

• Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, methodology): “With its original inspiration from [40], this sampling method is space filling and reflects the goal of sampling; it is a more aggressive MCS method.”
• Mode-pursuing sampling method for global optimization on expensive black-box functions (2004, methodology): “As has been shown in Fu and Wang [8], the sample drawn according to their algorithm is independent and has an asymptotic distribution g(x).”