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Bias and Variance in the Bayesian Subset Simulation Algorithm

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Abstract

The Bayesian Subset Simulation (BSS) algorithm is a recently proposed approach, based on Sequential Monte Carlo simulation and Gaussian process modeling, for the estimation of the probability that $f(X)$ exceeds some thresold $u$ when $f$ is expensive to evaluate and $P(f(X)>u)$ is small. We discuss in this talk the bias an variance of the BSS algorithm, and propose a variant where the bias-variance trade-off is automatically tuned.
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Dates and versions

hal-01377732 , version 1 (07-10-2016)

Identifiers

  • HAL Id : hal-01377732 , version 1

Cite

Julien Bect, Roman Sueur, Emmanuel Vazquez. Bias and Variance in the Bayesian Subset Simulation Algorithm. 2016 SIAM Conference on Uncertainty Quantification, Apr 2016, Lausanne, Switzerland. ⟨hal-01377732⟩
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