Skip to main content
Springer Nature Link
Log in

Monte-Carlo inference and its relations to reliable frequency identification

  • Conference paper
  • First Online:
Fundamentals of Computation Theory (FCT 1989)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 380))

Included in the following conference series:

  • 143 Accesses

Abstract

For EX and BC-type identification Monte-Carlo inference as well as reliable frequency identification on sets of functions are introduced. In particular, we relate the one to the other and characterize Monte-Carlo inference to exactly coincide with reliable frequency identification, on any set ℳ. Moreover, it is shown that reliable EX and BC-frequency inference forms a new discrete hierarchy having the breakpoints 1, 1/2, 1/3, ....

The results were obtained during the author's visit of the computing center of the Latvian State University.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  • ANGLUIN, D., AND SMITH, C. H. (1989), Inductive inference: theory and methods. Computing Surveys 15, 237–269

    Article  Google Scholar 

  • ANGLUIN, D., AND SMITH, C. H. (1986), Formal inductive inference, in Encyclopedia of Artificial Intelligence, (S. Shapiro, Ed.), to app.

    Google Scholar 

  • BARZDIN, YA. M. (1974), Two theorems on the limiting synthesis of functions, in Theory of Algorithms and Programs I, (Ya. M. Barzdin, Ed.), pp. 82–88, Latvian State University.

    Google Scholar 

  • Theory of Algorithms and Programs I, II, III, (1974), (1975), (1977) (Ya. M. Barzdin, Ed.). Latvian State University.

    Google Scholar 

  • BLUM, L. AND BLUM, M. (1975), Toward a mathematical theory of inductive inference. Inform. and Control 28, 122–155.

    Article  Google Scholar 

  • CASE, J., AND SMITH, C. H. (1989), Comparison of identification criteria for machine inductive inference. Theo. Comp. Sci. 25, 198–220

    Google Scholar 

  • DALEY, R. (1986), Towards the development of an analysis of learning algorithms, in Proc. Internat. Workshop of Analogical and Inductive Inference, Wendisch-Rietz, 1986, (K.P. Jantke, Ed.), Lect. Notes Comp. Sci. 265, pp. 1–18.

    Google Scholar 

  • GOLD, E. M. (1965), Limiting recursion. J. Symbolic Logic 30, 28–48.

    Google Scholar 

  • KINBER, E. B., AND ZEUGMANN, T. (1985), Inductive inference of almost everywhere correct programs by reliably working strategies. J. Inf. Processing and Cybernetics (EIK) 21, 91–100.

    Google Scholar 

  • KLETTE; R., AND WIEHAGEN, R. (1980). Research in the theory of inductive inference by GDR mathematicians — a survey. Inf. Sci. 22, 149–169.

    Google Scholar 

  • MACHTEY, M., AND YOUNG, P. (1978) "An Introduction to the General Theory of Algorithms", North-Holland, New York

    Google Scholar 

  • MINICOZZI, E. (1976), Some natural properties of strong-identification in inductive inference. Theo. Comp. Sci. 2, 345–360.

    Article  Google Scholar 

  • OSHERSON, D., STOB, M., AND WEINSTEIN, S. (1986), "Systems that Learn" MIT Press, Cambridge.

    Google Scholar 

  • PITT, L. B. (1984), A characterization of probabilistic inference. In Proc. 25th Annual Symp. Foundations of Comp. Sci., pp. 485–494.

    Google Scholar 

  • PITT, L. B. (1985), Probabilistic inductive inference. Yale University, YALEU/DCS/TR-400, Ph.D. Thesis.

    Google Scholar 

  • PODNIEKS, K.M. (1974), Comparing various concepts of function prediction and program synthesis I, in Theory of Algorithms and Programs, (Ya. M. Barzdin, Ed.), pp. 68–81, Latvian State University.

    Google Scholar 

  • PODNIEKS, K.M. (1975), Comparing various concepts of function prediction and program synthesis II, Theory of Algorithms and Programs, (Ya. M. Barzdin, Ed.), pp. 35–44 Latvian State University.

    Google Scholar 

  • ROGERS, H. JR. (1967) "Theory of Recursive Functions and Effective Computability", Mc-Graw Hill, New York

    Google Scholar 

  • SMITH, C. H. (1982), The power of pluralism for automatic program synthesis. Journal of the ACM 29, 1144–1165.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computing Center, Latvian State University, 29 Rainis Boulevard, 226250, Riga, U.S.S.R.

    Efim Kinber

  2. Department of Mathematics, Humboldt University at Berlin, P.O. Box 1297, 1086, Berlin, G.D.R.

    Thomas Zeugmann

Authors
  1. Efim Kinber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Zeugmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

J. Csirik J. Demetrovics F. Gécseg

Rights and permissions

Reprints and permissions

Copyright information

© 1989 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kinber, E., Zeugmann, T. (1989). Monte-Carlo inference and its relations to reliable frequency identification. In: Csirik, J., Demetrovics, J., Gécseg, F. (eds) Fundamentals of Computation Theory. FCT 1989. Lecture Notes in Computer Science, vol 380. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-51498-8_25

Download citation

  • DOI: https://doi.org/10.1007/3-540-51498-8_25

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-51498-5

  • Online ISBN: 978-3-540-48180-5

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation