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Derivatives of probability functions and some applications

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Abstract

Probability functions depending upon parameters are represented as integrals over sets given by inequalities. New derivative formulas for the intergrals over a volume are considered. Derivatives are presented as sums of integrals over a volume and over a surface. Two examples are discussed: probability functions with linear constraints (random right-hand sides), and a dynamical shut-down problem with sensors.

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References

  1. P. Bjerager,Methods for Structural Reliability Computations, Course on Reliability Problems: General Principles and Applications in Mechanics of Solids and Structures, International Center for Mechanical Sciences, Udine, Italy, CISM Lecture Notes (Springer, 1991).

  2. Yu. Ermoliev, V. Norkin and R. Wets, The minimization of discontinuous functions: mollifier subgradients, Working Paper WP-92-73, International Institute for Applied Systems Analysis, Laxenburg, Austria (1992).

    Google Scholar 

  3. Yu. Ermoliev, Stochastic quasi-gradient methods and their applications to system optimization, Stochastics 4 (1983) 1–36.

    Google Scholar 

  4. A.A. Gaivoronski, L.Y. Shi and R.S. Sreenivas, Augmented infinitesimal perturbation analysis: an alternative explanation, Discr. Event Dyn. Syst. 2 (1992) 121–138.

    Article  Google Scholar 

  5. P. Glasserman,Gradient Estimation Via Perturbation Analysis (Kluwer, Boston, 1991).

    Google Scholar 

  6. D.P. Graver and P.K. Samanta, Detecting component failure potential using proportional hazard model, US Nuclear Regulatory Commission, NUREG/CR-5324, BNL-NUREG-52188 (1989).

  7. Y.C. Ho and X.R. Cao,Perturbation Analysis of Discrete Event Dynamic Systems (Kluwer, Boston, 1991).

    Google Scholar 

  8. K. Marti, Stochastic optimization methods in structural design, ZAMM 4 (1990) T742-T745.

    Google Scholar 

  9. G.Ch. Pflug,Simulation and Optimization — the Interface (Kluwer, 1995), in press.

  10. A. Prékopa, On probabilistic constrained programming, in:Proc. Princeton Symp. on Mathematical Programming (Princeton University Press, Princeton, NJ, 1970) pp. 113–138.

    Google Scholar 

  11. A. Prékopa and T. Szántai, A new multivariate gamma distribution and its fitting to empirical streamflow data, Water Resources Res. 14 (1978) 19–24.

    Google Scholar 

  12. A. Pulkkinen and S. Uryasev, Optimal operational strategies for an inspected component — statement of the problem Working Paper WP-90-62, International Institute for Applied Systems Analysis, Laxenburg, Austria (1990).

    Google Scholar 

  13. A. Pulkkinen and S. Urya'sev, Optimal operational strategies for an inspected component — solution techniques, Collaborative Paper CP-91-13, International Institute for Applied Systems Analysis, Laxenburg, Austria (1991).

    Google Scholar 

  14. E. Raik, The differentiability in the parameter of the probability function and optimization of the probability function via the stochastic pseudogradient method, Izv. Akad. Nayk Est. SSR, Phis. Math. 24 (1975) 3–6 (in Russian).

    Google Scholar 

  15. N. Roenko, Stochastic programming problems with integral functionals over multivalued mappings, Synopsis of Ph.D. Thesis, Kiev, Ukraine (in Russian) (1983).

  16. R. Rubinstein, Sensitivity analysis of discrete event systems by the “push out” method, Ann. Oper. Res. 39 (1992) 229–250.

    Article  Google Scholar 

  17. R. Rubinstein and A. Shapiro,Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization via the Score Function Method (Wiley, Chichester, 1993).

    Google Scholar 

  18. P.K. Samanta, W.E. Vesely and I.S. Kim, Study of operational risk-based configuration control, US Nuclear Regulatory Commission, NUREG/CR-5641, BNL-NUREG-52261 (1991).

  19. J. Simon, Second variation in domain optimization problems, in:Int. Series of Numerical Mathematics, vol. 91, eds. F. Kappel, K. Kunish and W. Schappacher (Birkhäuser, 1985) pp. 361–378.

  20. S. Uryas'ev, Differentiability of an integral over a set defined by inclusion, Kibernetika (Kiev) 5 (1988) 83–86 (in Russian) [Transl.: Cybernetics 24 (1988) 638–742].

    Google Scholar 

  21. S. Uryas'ev, A differentiation formula for integrals over sets given by inclusion, Numer. Funct. Anal. Optim. 10 (1989) 827–841.

    Google Scholar 

  22. S. Uryas'ev, Derivatives of probability functions and integrals over sets given by inequalities, J. Comp. Appl. Math. 56 (1995).

  23. W.E. Vesely, Approaches for age-dependent probabilistic safety assessments with emphasis on prioritization and sensitivity studies, US Nuclear Regulatory Commission, NUREG/CR-5587, SAIC-92/1137 (1992).

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Uryasev, S. Derivatives of probability functions and some applications. Ann Oper Res 56, 287–311 (1995). https://doi.org/10.1007/BF02031712

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