Advertisement

Data Centric Science for Information Society

  • Genshiro Kitagawa
Conference paper

Abstract

Due to rapid development of information and communication technologies, the methodology of scientific research and the society itself are changing. The present grand challenge is the development of the cyber-enabled methodology for scientific researches to create knowledge based on large scale massive data. To realize this, it is necessary to develop a method of integrating various types of information. Thus the Bayes modeling becomes the key technology. In the latter half of the paper, we focus on time series and present general state-space model and related recursive filtering algorithms. Several examples are presented to show the usefulness of the general state-space model.

Keywords

Extended Kalman Filter Nonstationary Time Series Markov Switching Model Augmented State Vector Data Centric Science 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: Petrov BN, Csaki F (eds) Proc. 2nd international symposium on information theory. Akademiai Kiado, Budapest, 267–281Google Scholar
  2. 2.
    Akaike H (1980) Likelihood and the Bayes procedure. In: Bernardo JM, DeGroot MH, Lindley DV, Smith AFM (eds) Bayesian statistics. University Press, Valencia, 143–166Google Scholar
  3. 3.
    Anderson BDO, Moore JB (1979) Optimal filtering. Prentice-Hall, New JerseyMATHGoogle Scholar
  4. 4.
    Doucet A, Freitas F, Gordon N (2001) Sequential Monte Carlo methods in practice. Springer, New YorkMATHGoogle Scholar
  5. 5.
    Harrison PJ, Stevens CF (1976) Bayesian forecasting. J R Stat Soc B 38:205–247MathSciNetMATHGoogle Scholar
  6. 6.
    Kim CJ, Nelson CR (1999) State-space models with regime switching. MIT Press, Cambridge, MAGoogle Scholar
  7. 7.
    Kitagawa G (1983) Changing spectrum estimation. J Sound Vibration 89(3):433–445MathSciNetADSMATHCrossRefGoogle Scholar
  8. 8.
    Kitagawa G (1987) Non-Gaussian state-space modeling of nonstationary time series (with discussion). J Am Stat Assoc 82:1032–1063MathSciNetMATHGoogle Scholar
  9. 9.
    Kitagawa G (1996) Monte Carlo filter and smoother for non-Gaussian nonlinear state space model. J Comput Graph Stat 5:1–25MathSciNetGoogle Scholar
  10. 10.
    Kitagawa G (1998) Self-organizing state space model. J Am Stat Assoc 93:1203–1215Google Scholar
  11. 11.
    Kitagawa G, Gersch W (1996) Smoothness priors analysis of time series. Springer, New YorkMATHCrossRefGoogle Scholar
  12. 12.
    Kitagawa G, Sato S (2001) Monte Carlo smoothing and self-organizing state-space model. In: Doucet A, de Freitas N, Gordon N (eds) Sequential Monte Carlo methods in practice. Springer, New YorkGoogle Scholar
  13. 13.
    Konishi S, Kitagawa G (2008) Information criteria and statistical modeling. Springer, New YorkMATHCrossRefGoogle Scholar
  14. 14.
    Nakano S, Ueno G, Higuchi T (2007) Merging particle filter for sequential data assimilation. Nonlinear Process Geophys 14:395–408CrossRefGoogle Scholar
  15. 15.
    Tsubaki H (2002) Statistical science aspects of business. Proc Japan Soc Appl Sci 16:26–30 (in Japanese)Google Scholar

Copyright information

© Springer 2010

Authors and Affiliations

  1. 1.The Institute of Statistical MathematicsResearch Organization of Information and SystemsTachikawaJapan

Personalised recommendations