Appendix: Distribution Theory and Linear Algebra

  • Mike West
  • Jeff Harrison
Part of the Springer Series in Statistics book series (SSS)

Abstract

This Chapter contains a review and discussion of some of the basic results in distribution theory used throughout the book. Many of these results are stated without proofs and the reader is referred to a standard text on multivariate analysis, such as Mardia, Kent and Bibby (1979) for further details. The exceptions to this general rule are some of the results relating to Bayesian prior to posterior calculations in multivariate normal and joint normal/gamma models. Press (1985) is an excellent reference for such results in addition to standard, non-Bayesian multivariate theory. These results are discussed in detail as they are of some importance and should be clearly understood. For a comprehensive reference to all the distribution theory, see Johnson and Kotz (1972). For distribution theory specifically within the contexts of Bayesian analyses, see Aitchison and Dunsmore (1976), Box and Tiao (1973), and De Groot (1971).

Keywords

Marginal Distribution Variance Matrix Matrix Algebra Distribution Theory Multivariate Normal Distribution 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 1989

Authors and Affiliations

  • Mike West
    • 1
  • Jeff Harrison
    • 2
  1. 1.Institute of Statistics and Decision SciencesDuke UniversityDurhamUSA
  2. 2.Department of StatisticsUniversity of WarwickCoventryUK

Personalised recommendations