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A Risk-Sensitive Generalization of Maximum APosterior Probability (MAP) Estimation

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Stochastic Theory and Control

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 280))

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Abstract

A sequential filtering scheme for the risk-sensitive state estimation of partially observed Markov chains is presented. The previously introduced risk-sensitive filters are unified in the context of risk-sensitive Maximum A Posterior Probability (MAP) estimation. Structural results for the filter banks are given. The influence of the availability of information and the transition probabilities on the decision regions and the behavior of risk-sensitive estimators are studied.

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References

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering and Institute for Systems Research, University of Maryland, College Park, MD, 20742, USA

    Vahid Reza Ramezani & Steven I. Marcus

Authors
  1. Vahid Reza Ramezani
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  2. Steven I. Marcus
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Editor information

Editors and Affiliations

  1. Dep. of Mathematics, University of Kansas, 405 Snow Hall, Lawrence, KS, 66045, USA

    Bozenna Pasik-Duncan

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© 2002 Springer-Verlag Berlin Heidelberg

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Ramezani, V.R., Marcus, S.I. (2002). A Risk-Sensitive Generalization of Maximum APosterior Probability (MAP) Estimation. In: Pasik-Duncan, B. (eds) Stochastic Theory and Control. Lecture Notes in Control and Information Sciences, vol 280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48022-6_28

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  • DOI: https://doi.org/10.1007/3-540-48022-6_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43777-2

  • Online ISBN: 978-3-540-48022-8

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