کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1150855 1489821 2014 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Bayesian optimal sequential design for nonparametric regression via inhomogeneous evolutionary MCMC
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آمار و احتمال
پیش نمایش صفحه اول مقاله
Bayesian optimal sequential design for nonparametric regression via inhomogeneous evolutionary MCMC
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Statistical Methodology - Volume 18, May 2014, Pages 131–141
نویسندگان
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