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Bayesian Analysis of Sensory Inputs of a Mobile Robot

  • Conference paper
Case Studies in Bayesian Statistics

Part of the book series: Lecture Notes in Statistics ((LNS,volume 162))

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Abstract

This paper applies a novel Bayesian clustering method to identify characteristic dynamics of sensory inputs of a mobile robot. The method starts by transforming the sensory inputs into Markov chains and then applies our new agglomerative clustering procedure to discover the most probable set of clusters describing the robot’s experiences. To increase efficiency, the method uses an entropy-based heuristic search strategy.

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Authors
  1. Paola Sebastiani
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  2. Marco Ramoni
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  3. Paul Cohen
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Editor information

Editors and Affiliations

  1. Center for Statistical Sciences, Brown University, Box G-A416, Providence, RI, 02912, USA

    Constantine Gatsonis

  2. Department of Statistics, Carnegie Mellon University, Baker Hall 229, Pittsburgh, PA, 15213, USA

    Robert E. Kass

  3. Division of Biostatistics, University of Minnesota, Box 303, Mayo Building, Minneapolis, MN, 55455, USA

    Bradley Carlin

  4. Department of Statistics, Iowa State University, 222 Snedcor Hall, Ames, IA, 50011-1210, USA

    Alicia Carriquiry

  5. Department of Statistics, Columbia University, 618 Mathematics Building, New York, NY, 10027, USA

    Andrew Gelman

  6. Department of Statistics, Carnegie Mellon University, Baker Hall 232, Pittsburgh, PA, 15213, USA

    Isabella Verdinelli

  7. Institute of Statistics and Decision Sciences, Duke University, 214A Old Chemistry Building, Durham, NC, 27708-0251, USA

    Mike West

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© 2002 Springer Science+Business Media New York

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Sebastiani, P., Ramoni, M., Cohen, P. (2002). Bayesian Analysis of Sensory Inputs of a Mobile Robot. In: Gatsonis, C., et al. Case Studies in Bayesian Statistics. Lecture Notes in Statistics, vol 162. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-0035-9_11

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  • DOI: https://doi.org/10.1007/978-1-4613-0035-9_11

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-95169-0

  • Online ISBN: 978-1-4613-0035-9

  • eBook Packages: Springer Book Archive

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