2002Unpublished venueRequires access

Guaranteed bounds for probabilistic μ

S. Khatri, Pablo A. Parrilo

Open publisher page 12 citations

Abstract

Probabilistic extensions to /spl mu/ are formulated for the system context. In the application to systems these formulations are fundamentally mixed worst case and probabilistic uncertainties. A purely probabilistic /spl mu/ problem is proposed and algorithms are developed and implemented to compute guaranteed upper and lower bounds. These methods are variations on branch and bound algorithms. These methods are ideally suited for the probabilistic analysis of rare events thus filling the gap between Monte Carlo methods and worst case formulations.

About this research paper

What this paper is about

Probabilistic extensions to /spl mu/ are formulated for the system context. In the application to systems these formulations are fundamentally mixed worst case and probabilistic uncertainties. A purely probabilistic /spl mu/ problem is proposed and algorithms are developed and implemented to compute guaranteed upper and lower bounds. These methods are variations on branch and bound algorithms. These methods are ideally suited for the probabilistic analysis of rare events thus filling the gap between Monte Carlo methods and worst case formulations.

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OpenAlex reports 12 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Probabilistic extensions to /spl mu/ are formulated for the system context. In the application to systems these formulations are fundamentally mixed worst case and probabilistic uncertainties. A purely probabilistic /spl mu/ problem is proposed and algorithms are developed and implemented to compute guaranteed upper and lower bounds. These methods are variations on branch and bound algorithms. These methods are ideally suited for the probabilistic analysis of rare events thus filling the gap between Monte Carlo methods and worst case formulations.

Key concepts: Probabilistic logic, Probabilistic analysis of algorithms, Upper and lower bounds, Computer science, Monte Carlo method, Context (archaeology), Algorithm, Mathematical optimization

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