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- W2071222642 abstract "Abstract We develop a novel computational methodology for Bayesian optimal sequential design for nonparametric regression. This computational methodology, that we call inhomogeneous evolutionary Markov chain Monte Carlo, combines ideas of simulated annealing, genetic or evolutionary algorithms, and Markov chain Monte Carlo. Our framework allows optimality criteria with general utility functions and general classes of priors for the underlying regression function. We illustrate the usefulness of our novel methodology with applications to experimental design for nonparametric function estimation using Gaussian process priors and free-knot cubic splines priors." @default.
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- W2071222642 date "2014-05-01" @default.
- W2071222642 modified "2023-09-24" @default.
- W2071222642 title "Bayesian optimal sequential design for nonparametric regression via inhomogeneous evolutionary MCMC" @default.
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- W2071222642 doi "https://doi.org/10.1016/j.stamet.2013.11.002" @default.
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