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The Complexity of Plate Probabilistic Models

  • Fabio G. CozmanEmail author
  • Denis D. Mauá
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9310)

Abstract

Plate-based probabilistic models combine a few relational constructs with Bayesian networks, so as to allow one to specify large and repetitive probabilistic networks in a compact and intuitive manner. In this paper we investigate the combined, data and domain complexity of plate models, showing that they range from polynomial to \(\#\mathsf {P}\)-complete to \(\#\mathsf {EXP}\)-complete.

Keywords

Plate Model Relational Bayesian Networks Counting Turing Machine Nested Plates Combined Complexity 
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.

Notes

Acknowledgements

The first author was partially supported by CNPq and the second author was partially supported by FAPESP.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Universidade de São PauloSão PauloBrazil

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