Skip to main content
Log in

Possibility of approximating multimodal distributions by mixtures of standard probability density functions

  • General Aspects of Metrology and Measurement Techniques
  • Published:
Measurement Techniques Aims and scope

Abstract

Specific aspects of the approximation of multirnodal distributions are discussed. It is shown that the formulation of an algorithm for the approximation of multimodal probability density functions by mixtures of standard distributions is a solvable problem, since it is proved that a finite mixture of distributions belonging to the author's “extended” Pearson system is separable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. M. G. Kendall and A. Stuart, The Advanced Theory of Statistics, Vol. 1: Distribution Theory, 4th ed. Griffin, London (1977).

    Google Scholar 

  2. G. J. Hahn and S. S. Shapiro, Statistical Models in Engineering, Wiley, New York (1967).

    Google Scholar 

  3. S. V. Prokopchina, Yu. G. Rubinshtein, and Yu. M. Lipovetskii, in: Proceedings of the Republic Scientific-Technical Conference of the Youth of Udmurtia for the Acceleration of Scientific—Technical Progress [inRussian], Part III, Izhevsk (1987), p. 334.

  4. A. V. Milen'kii, Classification of Signals in the Presence of Uncertainty [in Russian], Sov. Radio, Moscow (1975).

    Google Scholar 

  5. F. Oberhettinger, Fourier Transforms of Distributions and Their Inverses, Academic Press, New York (1973).

    Google Scholar 

Download references

Authors

Additional information

Translated from Izmeritel'naya Tekhnika, No. 8, pp. 13–16, August, 1993.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rubinshtein, Y.G. Possibility of approximating multimodal distributions by mixtures of standard probability density functions. Meas Tech 36, 858–864 (1993). https://doi.org/10.1007/BF00983979

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00983979

Keywords

Navigation