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Error-Bounded Probabilistic Computations between MA and AM

  • Elmar Böhler
  • Christian Glaßer
  • Daniel Meister
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2747)

Abstract

We introduce the probabilistic complexity class SBP. This class emerges from BPP by keeping the promise of a probability gap but decreasing the probability limit to exponentially small values. We locate SBP in the polynomial-time hierarchy, more precisely, between MA and AM. We provide evidence that SBP does not coincide with these and other known complexity classes. We construct an oracle relative to which SBP is not contained in \({\mathrm{\Sigma^P_{2}}}\).

We provide a new characterization of BPPpath. This characterization shows that SBP is a subset of BPPpath. Consequently, there is an oracle relative to which BPPpath is not contained in \({\mathrm{\Sigma^P_{2}}}\).

Keywords

Turing Machine Complexity Class Proof System Probability Limit Probabilistic Machine 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Elmar Böhler
    • 1
  • Christian Glaßer
    • 1
  • Daniel Meister
    • 1
  1. 1.Theoretische InformatikUniversität WürzburgWürzburgGermany

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