Multinormal probability by sequential conditioned importance sampling: Theory and application

Probabilistic Engineering Mechanics

Volumn 13, Issue 4, 1998, Pages 299-308

  Ambartzumian, R ^a   Der Kiureghian, A ^b   Ohanian, V ^a   Sukiasian, H ^a  

^a INSTITUTE OF ORGANIC CHEMISTRY   (Armenia)

^b Berkeley   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; EFFICIENCY; MONTE CARLO METHODS; PROBABILITY; SENSITIVITY ANALYSIS;

SAMPLING;

EID: 0032188563     PISSN: 02668920     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0266-8920(98)00003-4     Document Type: Article

References (10)

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3. Gollwitzer, S. and Rackwitz, R. An efficient numerical solution to the multinormal integral. Probabilistic Engineering Mechanics, 1988, 3(2), 98-101.
4. Genz, A. Numerical computation of multivariate normal probabilities. J. Computational and Graphical Statistics, 1992, 1, 141-149.
5. Joe, H. Approximations to multivariate normal rectangle probabilities based on conditional expectations. J. American Statistical Association, 1995, 90(431), 957-964.
6. Rubenstein, R. Y., Simulation and the Monte Carlo Method. John Wiley, New York, 1981.
7. Dunnett, C. W. and Sobel, M. Approximations to the probability integral and certain percentage points of a multivariate analogue of Student's t-distribution. Biometrika, 1955, 42, 258-260.
8. Melchers, R., Structural Reliability, Analysis and Prediction. John Wiley, New York, 1987.
9. Hohenbichler, M. and Rackwitz, R. Sensitivity and importance measures in structural reliability. Civil Engineering Systems, 1986, 3, 203-210.
10. Bjerager, P. and Krenk, S. Parametric sensitivity in first-order reliability theory. J. Engineering Mechanics, ASCE, 1989, 115(7), 1577-1582