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Modeling and Optimization: Theory and Applications pp 1–28Cite as

Approximate Bayesian inference for simulation and optimization

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Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 147))

Abstract

We present an overview of approximate Bayesian methods for sequential learning in problems where conjugate Bayesian priors are unsuitable or unavailable. Such problems have numerous applications in simulation optimization, revenue management, e-commerce, and the design of competitive events. We discuss two important computational strategies for learning in such applications, and illustrate each strategy with multiple examples from the recent literature. We also briefly describe conjugate Bayesian models for comparison, and remark on the theoretical challenges of approximate models.

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Author information

Authors and Affiliations

  1. Robert H. Smith School of Business, University of Maryland, College Park, MD, USA

    Ilya O. Ryzhov

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  1. Ilya O. Ryzhov
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Correspondence to Ilya O. Ryzhov .

Editor information

Editors and Affiliations

  1. Dept. of Industrial & Systems Eng., Lehigh University, Bethlehem, Pennsylvania, USA

    Boris Defourny

  2. Dept. of Industrial & Systems Eng., Lehigh University, Bethlehem, Pennsylvania, USA

    Tamás Terlaky

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Ryzhov, I.O. (2015). Approximate Bayesian inference for simulation and optimization. In: Defourny, B., Terlaky, T. (eds) Modeling and Optimization: Theory and Applications. Springer Proceedings in Mathematics & Statistics, vol 147. Springer, Cham. https://doi.org/10.1007/978-3-319-23699-5_1

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