Corpus ID: 30816233

Bayesian design of experiments for industrial and scientific applications via Gaussian processes

@inproceedings{Woods2016BayesianDO,
  title={Bayesian design of experiments for industrial and scientific applications via Gaussian processes},
  author={D. Woods},
  year={2016}
}
The design of an experiment can be considered to be at least implicitly Bayesian, with prior knowledge used informally to aid decisions such as the variables to be studied and the choice of a plausible relationship between the explanatory variables and measured responses. Bayesian methods allow uncertainty in these decisions to be incorporated into design selection through prior distributions that encapsulate information available from scientific knowledge or previous experimentation. Further… Expand

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