Abstract
Matrix algebra is the natural tool for the study of linear stochastic models with many parameters. Complete solutions are given for the nonconfluent and the general confluent cases. It is shown that the axiomatics of a generalized linear stochastic model are naturally described within the framework of the linear algebra in an Euclidean space.
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Literature
Bush, R. R. and F. Mosteller. 1955.Stochastic Models for Learning. New York: John Wiley & Sons, Inc.
Goldberg, S. 1961.Introduction to Difference Equations. New York: Science Editions.
Lichnerowicz, A. 1947.Algebre et Analyse Lineaires. Paris: Masson.
Turnbull, H. W. and A. C. Aitkin. 1952.An Introduction to the Theory of Canonical Matrices. London: Black and Son Ltd.
Williams, E. 1959.Regression Analysis. New York: John Wiley & Sons, Inc.
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Marmasse, C. Matrix algebra of a generalized linear stochastic model. Nonconfluent and general confluent cases. Bulletin of Mathematical Biophysics 27 (Suppl 1), 311–315 (1965). https://doi.org/10.1007/BF02477286
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DOI: https://doi.org/10.1007/BF02477286