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• W807293800 abstract "Drawing a sample from a discrete distribution is one of the building components for Monte Carlo methods. Like other sampling algorithms, discrete sampling also suffers from high computational burden in large-scale inference problems. We study the problem of sampling a discrete random variable with a high degree of dependency that is typical in large-scale Bayesian inference and graphical models, and propose an efficient approximate solution with a subsampling approach. We make a novel connection between the discrete sampling and Multi-Armed Bandits problems with a finite reward population and provide three algorithms with theoretical guarantees. Empirical evaluations show the robustness and efficiency of the approximate algorithms in both synthetic and real-world large-scale problems." @default.
• W807293800 created "2016-06-24" @default.
• W807293800 creator A5010820865 @default.
• W807293800 creator A5074021977 @default.
• W807293800 date "2015-06-30" @default.
• W807293800 modified "2023-09-27" @default.
• W807293800 title "Subsampling-Based Approximate Monte Carlo for Discrete Distributions." @default.
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• W807293800 hasPublicationYear "2015" @default.
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