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Probabilistic Bisection Converges Almost as Quickly as Stochastic Approximation by Frazier, Peter I.; Henderson, Shane G.; Waeber, Rolf is a scholarly article available to read on EtoBox.

What is Probabilistic Bisection Converges Almost as Quickly as Stochastic Approximation about?

The probabilistic bisection algorithm (PBA) solves a class of stochastic root-finding problems in one dimension by successively updating a prior belief on the location of the root based on noisy responses to queries at chosen points. The responses indicate the direction of the root from the queried point, and are incorrect with a fixed probability. The fixed-probability assumption is problematic in applications, and so we extend the PBA to apply when this assumption is relaxed. The extension involves the use of a power-one test at each queried point. We explore the convergence behavior of the extended PBA, showing that it converges at a rate arbitrarily close to, but slower than, the canonical "square root" rate of stochastic approximation.

Author

Frazier, Peter I.; Henderson, Shane G.; Waeber, Rolf

Published

2016

Language

EN