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Monte Carlo Methods

  • Karl-Rudolf KochEmail author
Living reference work entry
Part of the Springer Reference Naturwissenschaften book series (SRN)

Zusammenfassung

Monte-Carlo-Methoden arbeiten mit Zufallszahlen aus Verteilungsfunktionen, um unbekannte Parameter oder allgemeine Funktionen unbekannter Parameter zu schätzen, und um ihre Erwartungswerte, Varianzen und Kovarianzen zu berechnen. Im allgemeinen nutzt man wegen des zentralen Grenzwertsatzes die multivariate Normalverteilung. Wenn jedoch Zufallsvariable mit der Normalverteilung mit Zufallsvariablen unterschiedlicher Verteilungen kombiniert werden, gilt die Normalverteilung nicht mehr. Die Monte-Carlo-Methode wird dann benötigt, die Erwartungswerte, Varianzen und Kovarianzen der Zufallsvariablen mit Verteilungen zu erhalten, die sich von der Normalverteilung unterscheiden.

Die Fehlerfortpflanzung durch die Monte-Carlo-Methode wird diskutiert und Methoden für die Generierung von Zufallswerten aus der multivariaten Normalverteilung und aus der multivariaten Gleichverteilung. Die Monte-Carlo-Integration der wesentlichen Stichprobe führt auf den SIR (sampling-importance-resampling) Algorithmus. Monte-Carlo-Methoden mit Markoff-Ketten verschaffen durch den Metropolis-Algorithmus und das Gibbs-Verfahren weitere Methoden, Zufallswerte zu generieren. Als besondere Aufgabe wird das Gibbs-Verfahren zur Berechnung und Propagation großer Kovarianzmatrizen behandelt. Dieses Problem tritt auf, wenn das Schwerefeld der Erde aus Satelltenbeobachtungen bestimmt wird. Das Beispiel der minimal aufzudekkenden Ausreißer zeigt, wie die Monte-Carlo-Methode benutzt wird, um die Trennschärfe eines Hypothesentests zu bestimmen.

Keywords

Bayesian statistics SIR algorithm Metropolis algorithm Gibbs sampler Markov Chain Monte Carlo method 

Abstract

Monte Carlo methods deal with generating random variates from probability density functions in order to estimate unknown parameters or general functions of unknown parameters and to compute their expected values, variances and covariances. One generally works with the multivariate normal distribution due to the central limit theorem. However, if random variables with the normal distribution and random variables with a different distribution are combined, the normal distribution is not valid anymore. The Monte Carlo method is then needed to get the expected values, variances and covariances for the random variables with distributions different from the normal distribution.

The error propagation by the Monte Carlo method is discussed and methods for generating random variates from the multivariate normal distribution and from the multivariate uniform distribution. The Monte Carlo integration is presented leading to the sampling-importance-resampling (SIR) algorithm. Markov Chain Monte Carlo methods provide by the Metropolis algorithm and the Gibbs sampler additional ways of generating random variates. A special topic is the Gibbs sampler for computing and propagating large covariance matrices. This task arises when the geopotential is determined from satellite observations. The example of the minimal detectable outlier shows, how the Monte Carlo method is used to determine the power of a hypothesis test.

Notes

Acknowledgements

The author is indebted to Willi Freeden for his invitation to this contribution for HbMG and to Jan Martin Brockmann for his valuable comments.

References

  1. 1.
    Acko, B., Godina, A.: Verification of the conventional measuring uncertainty evaluation model with Monte Carlo simulation. Int. J. Simul. Model. 4, 76–84 (2005)CrossRefGoogle Scholar
  2. 2.
    Alkhatib, H., Kutterer, H.: Estimation of measurement uncertainty of kinematic TLS observation process by means of Monte-Carlo methods. J. Applied Geodesy 7, 125–133 (2013)CrossRefGoogle Scholar
  3. 3.
    Alkhatib, H., Neumann, I., Kutterer, H.: Uncertainty modeling of random and systematic errors by means of Monte Carlo and fuzzy techniques. J. Applied Geodesy 3, 67–79 (2009)CrossRefGoogle Scholar
  4. 4.
    Alkhatib, H., Schuh, W.D.: Integration of the Monte Carlo covariance estimation strategy into tailored solution procedures for large-scale least squares problems. J. Geodesy 81, 53–66 (2007)CrossRefGoogle Scholar
  5. 5.
    Arnold, S.: The Theory of Linear Models and Multivariate Analysis. Wiley, New York (1981)Google Scholar
  6. 6.
    Baarda, W.: Statistical Concepts in Geodesy. Publications on Geodesy, vol. 2, Nr. 4. Netherlands Geodetic Commission, Delft (1967)Google Scholar
  7. 7.
    Baarda, W.: A Testing Procedure for Use in Geodetic Networks. Publications on Geodesy, vol. 2, Nr. 5. Netherlands Geodetic Commission, Delft (1968)Google Scholar
  8. 8.
    Baselga, S.: Nonexistence of rigorous tests for multiple outlier detection in least-squares adjustment. J. Surv. Eng. 137, 109–112 (2011)CrossRefGoogle Scholar
  9. 9.
    Beckman, R., Cook, R.: Outlier….s. Technometrics 25, 119–149 (1983)Google Scholar
  10. 10.
    Besag, J.: Spatial interaction and the statistical analysis of lattice systems. J. R. Stat. Soc. B 36, 192–236 (1974)Google Scholar
  11. 11.
    Box, G., Muller, M.: A note on the generation of random normal deviates. Ann. Math. Stat. 29, 610–611 (1958)CrossRefGoogle Scholar
  12. 12.
    Cramér, H.: Mathematical Methods of Statistics. Princeton University Press, Princeton (1946)Google Scholar
  13. 13.
    Dagpunar, J.: Principles of Random Variate Generation. Clarendon Press, Oxford (1988)Google Scholar
  14. 14.
    Devroye, L.: Non-Uniform Random Variate Generation. Springer, Berlin (1986)CrossRefGoogle Scholar
  15. 15.
    Dietrich, C.: Uncertainty, Calibration and Probability, 2nd edn. Taylor & Francis, Boca Raton (1991)Google Scholar
  16. 16.
    van Dorp, J., Kotz, S.: Generalized trapezoidal distributions. Metrika 58, 85–97 (2003)CrossRefGoogle Scholar
  17. 17.
    Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. Comput. 10, 197–208 (2000)CrossRefGoogle Scholar
  18. 18.
    Falk, M.: A simple approach to the generation of uniformly distributed random variables with prescribed correlations. Commun. Stat. Simul. 28, 785–791 (1999)CrossRefGoogle Scholar
  19. 19.
    Gaida, W., Koch, K.R.: Solving the cumulative distribution function of the noncentral F-distribution for the noncentrality parameter. Sci. Bull. Stanislaw Staszic Univ. Min. Metall. Geodesy b.90(1024), 35–44 (1985)Google Scholar
  20. 20.
    Gelfand, A., Smith, A.: Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85, 398–409 (1990)CrossRefGoogle Scholar
  21. 21.
    Gelman, A., Carlin, J., Stern, H., Rubin, D.: Bayesian Data Analysis, 2nd edn. Chapman and Hall, Boca Raton (2004)Google Scholar
  22. 22.
    Geman, D., Geman, S., Graffigne, C.: Locating texture and object boundaries. In: Devijver, P., Kittler, J. (eds.) Pattern Recognition Theory and Applications, pp. 165–177. Springer, Berlin (1987)CrossRefGoogle Scholar
  23. 23.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. PAMI–6, 721–741 (1984)CrossRefGoogle Scholar
  24. 24.
    Geman, S., McClure, D.: Statistical methods for tomographic image reconstruction. Bull. Int. Stat. Inst. 52-21.1, 5–21 (1987)Google Scholar
  25. 25.
    Gentle, J.: Random Number Generation and Monte Carlo Methods, 2nd edn. Springer, Berlin (2003)Google Scholar
  26. 26.
    Gilks, W.: Full conditional distributions. In: Gilks, W., Richardson, S., Spiegelhalter, D. (eds.) Markov Chain Monte Carlo in Practice, pp. 75–88. Chapman and Hall, London (1996)Google Scholar
  27. 27.
    Golub, G., van Loan, C.: Matrix Computations. The Johns Hopkins University Press, Baltimore (1984)Google Scholar
  28. 28.
    Gordon, N., Salmond, D.: Bayesian state estimation for tracking and guidance using the bootstrap filter. J. Guid. Control. Dyn. 18, 1434–1443 (1995)CrossRefGoogle Scholar
  29. 29.
    Gundlich, B., Koch, K.R., Kusche, J.: Gibbs sampler for computing and propagating large covariance matrices. J. Geod. 77, 514–528 (2003)CrossRefGoogle Scholar
  30. 30.
    Gundlich, B., Kusche, J.: Monte Carlo integration for quasi–linear models. In: Xu, P., Liu, J., Dermanis, A. (eds.) VI Hotine-Marussi Symposium on Theoretical and Computational Geodesy, pp. 337–344. Springer, Berlin/Heidelberg (2008)CrossRefGoogle Scholar
  31. 31.
    Guo, J.F., Ou, J.K., Yuan, Y.B.: Reliability analysis for a robust M-estimator. J. Surv. Eng. 137, 9–13 (2011)CrossRefGoogle Scholar
  32. 32.
    Hennes, M.: Konkurrierende Genauigkeitsmaße – Potential und Schwächen aus der Sicht des Anwenders. Allgemeine Vermessungs-Nachrichten 114, 136–146 (2007)Google Scholar
  33. 33.
    Huber, P.: Robust estimation of a location parameter. Ann. Math. Stat. 35, 73–101 (1964)CrossRefGoogle Scholar
  34. 34.
    ISO: Guide to the Expression of Uncertainty in Measurement. International Organization for Standardization, Geneve (1995)Google Scholar
  35. 35.
    JCGM: Evaluation of measurement data–supplement 2 to the “Guide to the Expression of Uncertainty in Measurement”–Extension to any number of output quantities. JCGM 102:2011. Joint Committee for Guides in Metrology. (2011). www.bipm.org/en/publications/guides
  36. 36.
    Kacker, R., Jones, A.: On use of Bayesian statistics to make the guide to the expression of uncertainty in measurement consistent. Metrologia 40, 235–248 (2003)CrossRefGoogle Scholar
  37. 37.
    Kargoll, B.: On the Theory and Application of Model Misspecification Tests in Geodesy. Universität Bonn, Institut für Geodäsie und Geoinformation, Schriftenreihe 8, Bonn (2008)Google Scholar
  38. 38.
    Knight, N., Wang, J., Rizos, C.: Generalised measures of reliability for multiple outliers. J. Geod. 84, 625–635 (2010)CrossRefGoogle Scholar
  39. 39.
    Koch, K.R.: Ausreißertests und Zuverlässigkeitsmaße. Vermessungswesen und Raumordnung 45, 400–411 (1983)Google Scholar
  40. 40.
    Koch, K.R.: Parameter Estimation and Hypothesis Testing in Linear Models, 2nd edn. Springer, Berlin (1999)CrossRefGoogle Scholar
  41. 41.
    Koch, K.R.: Monte-Carlo-Simulation für Regularisierungsparameter. ZfV–Z Geodäsie, Geoinformation und Landmanagement 127, 305–309 (2002)Google Scholar
  42. 42.
    Koch, K.R.: Determining the maximum degree of harmonic coefficients in geopotential models by Monte Carlo methods. Studia Geophysica et Geodaetica 49, 259–275 (2005)CrossRefGoogle Scholar
  43. 43.
    Koch, K.R.: Gibbs sampler by sampling-importance-resampling. J. Geod. 81, 581–591 (2007)CrossRefGoogle Scholar
  44. 44.
    Koch, K.R.: Introduction to Bayesian Statistics, 2nd edn. Springer, Berlin (2007)Google Scholar
  45. 45.
    Koch, K.R.: Determining uncertainties of correlated measurements by Monte Carlo simulations applied to laserscanning. J. Appl. Geod. 2, 139–147 (2008)Google Scholar
  46. 46.
    Koch, K.R.: Evaluation of uncertainties in measurements by Monte Carlo simulations with an application for laserscanning. J. Appl. Geod. 2, 67–77 (2008)Google Scholar
  47. 47.
    Koch, K.R.: Minimal detectable outliers as measures of reliability. J. Geod. 89, 483–490 (2015)CrossRefGoogle Scholar
  48. 48.
    Koch, K.R.: Bayesian statistics and Monte Carlo methods. J. Geod. Sci. 8, 18–29 (2018)CrossRefGoogle Scholar
  49. 49.
    Koch, K.: Monte Carlo methods. GEM–Int. J. Geomath. 9(1), 117–143 (2018)CrossRefGoogle Scholar
  50. 50.
    Koch, K.R., Brockmann, J.: Systematic effects in laser scanning and visualization by confidence regions. J. Appl. Geod. 10(4), 247–257 (2016)Google Scholar
  51. 51.
    Koch, K.R., Kargoll, B.: Outlier detection by the EM algorithm for laser scanning in rectangular and polar coordinate systems. J. Appl. Geod. 9, 162–173 (2015)Google Scholar
  52. 52.
    Koch, K.R., Kusche, J., Boxhammer, C., Gundlich, B.: Parallel Gibbs sampling for computing and propagating large covariance matrices. ZfV–Z Geodäsie, Geoinformation und Landmanagement 129, 32–42 (2004)Google Scholar
  53. 53.
    Koch, K.R., Schmidt, M.: Deterministische und stochastische Signale. Dümmler, Bonn (1994). ftp://skylab.itg.uni-bonn.de/koch/00_textbooks/Determ_u_stoch_Signale.pdf
  54. 54.
    Kok, J.: Statistical analysis of deformation problems using Baarda’s testing procedures in: “Forty Years of Thought”. Anniversary Volume Occasion of Prof. Baarda’s 65th Birthday 2, 470–488 (1982). DelftGoogle Scholar
  55. 55.
    Kok, J.: On data snooping and multiple outlier testing. In: NOAA Technical Report NOS NGS 30. US Department of Commerce, National Geodetic Survey, Rockville (1984)Google Scholar
  56. 56.
    Lehmann, R.: Improved critical values for extreme normalized and studentized residuals in Gauss-Markov models. J. Geod. 86, 1137–1146 (2012)CrossRefGoogle Scholar
  57. 57.
    Lehmann, R.: On the formulation of the alternative hypothesis for geodetic outlier detection. J. Geod. 87, 373–386 (2013)CrossRefGoogle Scholar
  58. 58.
    Leonard, T., Hsu, J.: Bayesian Methods. Cambridge University Press, Cambridge (1999)Google Scholar
  59. 59.
    Liu, J.: Monte Carlo Strategies in Scientific Computing. Springer, Berlin (2001)Google Scholar
  60. 60.
    Marsaglia, G., Bray, T.: A convenient method for generating normal variables. SIAM Rev. 6, 260–264 (1964)CrossRefGoogle Scholar
  61. 61.
    Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21, 1087–1092 (1953)CrossRefGoogle Scholar
  62. 62.
    Nowel, K.: Application of Monte Carlo method to statistical testing in deformation analysis based on robust M-estimation. Surv. Rev. 48(348), 212–223 (2016)CrossRefGoogle Scholar
  63. 63.
    O’Hagan, A.: Bayesian Inference, Kendall’s Advanced Theory of Statistics, vol. 2B. Wiley, New York (1994)Google Scholar
  64. 64.
    Pope, A.: The statistics of residuals and the detection of outliers. In: NOAA Technical Report NOS65 NGS1. US Department of Commerce, National Geodetic Survey, Rockville (1976)Google Scholar
  65. 65.
    Proszynski, W.: Another approach to reliability measures for systems with correlated observations. J. Geod. 84, 547–556 (2010)CrossRefGoogle Scholar
  66. 66.
    Roberts, G., Smith, A.: Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms. Stochastic Process. Appl. 49, 207–216 (1994)CrossRefGoogle Scholar
  67. 67.
    Rubin, D.: Using the SIR algorithm to simulate posterior distributions. In: Bernardo, J., DeGroot, M., Lindley, D., Smith, A. (eds.) Bayesian Statistics, vol. 3, pp. 395–402. Oxford University Press, Oxford (1988)Google Scholar
  68. 68.
    Rubinstein, R.: Simulation and the Monte Carlo Method. Wiley, New York (1981)CrossRefGoogle Scholar
  69. 69.
    Schader, M., Schmid, F.: Distribution function and percentage points for the central and noncentral F-distribution. Stat. Pap. 27, 67–74 (1986)Google Scholar
  70. 70.
    Siebert, B., Sommer, K.D.: Weiterentwicklung des GUM und Monte-Carlo-Techniken. tm–Technisches Messen 71, 67–80 (2004)CrossRefGoogle Scholar
  71. 71.
    Smith, A., Gelfand, A.: Bayesian statistics without tears: a sampling-resampling perspective. Am. Stat. 46, 84–88 (1992)Google Scholar
  72. 72.
    Smith, A., Roberts, G.: Bayesian computation via the Gibbs sampler and related Markov Chain Monte Carlo methods. J. R. Stat. Soc. B 55, 3–23 (1993)Google Scholar
  73. 73.
    Staff of the Geodetic Computing Center, S.: The Delft approach for the design and computation of geodetic networks. In: “Forty Years of Thought”. Anniversary Volume on the Occasion of Prof. Baarda’s 65th Birthday 1, 202–274 (1982). DelftGoogle Scholar
  74. 74.
    Teunissen, P.: Adjusting and testing with the models of the affine and similarity transformation. Manuscr. Geodaet. 11, 214–225 (1986)Google Scholar
  75. 75.
    Teunissen, P.: Testing theory; An introduction. MGP, Department of Mathematical Geodesy and Positioning, Delft University of Technology, Delft (2000)Google Scholar
  76. 76.
    Teunissen, P., de Bakker, P.: Single-receiver single-channel multi-frequency GNSS integrity: outliers, slips, and ionospheric disturbances. J. Geod. 87, 161–177 (2013)CrossRefGoogle Scholar
  77. 77.
    Wilks, S.: Mathematical Statistics. Wiley, New York (1962)Google Scholar
  78. 78.
    Xu, P.: Random simulation and GPS decorrelation. J. Geod. 75, 408–423 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature 2018

Authors and Affiliations

  1. 1.Institute for Geodesy and Geoinformation, Theoretical Geodesy GroupUniversity of BonnBonnGermany

Section editors and affiliations

  • Willi Freeden
    • 1
  1. 1.Geomathematics GroupUniversity of KaiserslauternKaiserslauternGermany

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