Bayesian Cubic Spline in Computer Experiments

Abstract

Cubic splines are commonly used in numerical analysis. It has also become popular in the analysis of computer experiments, thanks to its adoption by the software JMP8.0.2 2010. In this paper a Bayesian version of the cubic spline method is proposed, in which the random function that represents prior uncertainty about y is taken to be aspecific stationary Gaussian process and y is the output of the computer experiment.An MCMC procedure is developed for updating the prior given the observed y values. Simulation examples and a real data application are given to show that the proposed Bayesian method performs better than the frequentist cubic spline method and the standard method based on the Gaussian correlation function.

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Document Details

Document Type
Technical Report
Publication Date
Jul 23, 2015
Accession Number
AD1000456

Entities

People

  • Yijie D Wang

Organizations

  • Georgia Tech Research Corporation

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Bayesian Networks
  • Computational Science
  • Computations
  • Data Science
  • Gaussian Processes
  • Information Science
  • Markov Chains
  • Maximum Likelihood Estimation
  • Monte Carlo Method
  • Numerical Analysis
  • Probability
  • Regression Analysis
  • Statistical Algorithms
  • Statistics
  • Stochastic Processes
  • Surveys

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Nuclear Civil Defense.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference