The point‐estimate method with large numbers of variables

By J. Christian, G. Baecher. 2002

81 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Safety, Risk, Reliability and Quality, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/6a9e839f1e0608ecbb5d980e4097cbc860c2d7b0

How researchers see this paper

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

In Civil and Structural Engineering

1. Hyuck-Jin Park
2. Jeong Woo Kim
3. J. Um
4. I. Woo

In Safety, Risk, Reliability and Quality

1. Juan Pérez-Ruiz
2. Hyuck-Jin Park
3. Jeong Woo Kim
4. J. Um
5. I. Woo
6. Juan M. Morales
7. J. Usaola
8. Curran Crawford

In Statistics and Probability

1. Hyuck-Jin Park
2. Jeong Woo Kim
3. J. Um
4. I. Woo
5. Juan M. Morales
6. J. Usaola
7. Curran Crawford
8. T. Williams

Citations in the corpus

• Application of fuzzy set theory to evaluate the probability of failure in rock slopes (2012, methodology): “Like FOSM, the point estimate method does not require knowledge of the probability density function for input, but it can still be accurate in many practical situations despite its simplicity (Christian and Baecher, 2002).”
• Point Estimate Schemes to Solve the Probabilistic Power Flow (2007, background): “by all schemes, has been pointed out in [31].”