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Monte Carlo Algorithms for Finding the Maximum of a Random Walk with Negative Drift

Research output: Contribution to journal › Article › Research › peer review

Authors

  • Ludwig Baringhaus
  • Rudolf Grübel

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Details

Original languageEnglish
Pages (from-to)74-86
Number of pages13
JournalJournal of Applied Probability
Volume43
Issue number1
Publication statusPublished - 2006

Abstract

We discuss two Monte Carlo algorithms for finding the global maximum of a simple random walk with negative drift. This problem can be used to connect the analysis of random input Monte Carlo algorithms with ideas and principles from mathematical statistics.

Keywords

    Brownian motion with drift, Conditioning, Efficiency, Fast Fourier transform, Ladder variable, Markov chain, Randomization

ASJC Scopus subject areas

Cite this

Monte Carlo Algorithms for Finding the Maximum of a Random Walk with Negative Drift. / Baringhaus, Ludwig; Grübel, Rudolf.
In: Journal of Applied Probability, Vol. 43, No. 1, 2006, p. 74-86.

Research output: Contribution to journal › Article › Research › peer review

Baringhaus L, Grübel R. Monte Carlo Algorithms for Finding the Maximum of a Random Walk with Negative Drift. Journal of Applied Probability. 2006;43(1):74-86. doi: 10.1239/jap/1143936244
Baringhaus, Ludwig ; Grübel, Rudolf. / Monte Carlo Algorithms for Finding the Maximum of a Random Walk with Negative Drift. In: Journal of Applied Probability. 2006 ; Vol. 43, No. 1. pp. 74-86.
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