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

Figures from this paper

References

SHOWING 1-10 OF 22 REFERENCES
Bayesian Design of Experiments Using Approximate Coordinate Exchange
  • 57
  • PDF
A review of modern computational algorithms for Bayesian optimal design
  • 111
  • Highly Influential
  • PDF
Towards Bayesian experimental design for nonlinear models that require a large number of sampling times
  • 37
  • Highly Influential
  • PDF
Bayesian experimental design for models with intractable likelihoods.
  • 44
  • Highly Influential
  • PDF
Fully Bayesian Experimental Design for Pharmacokinetic Studies
  • 29
  • PDF
Optimal Bayesian Design by Inhomogeneous Markov Chain Simulation
  • 167
Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data
  • 36
  • PDF
Optimal Design via Curve Fitting of Monte Carlo Experiments
  • 140
  • Highly Influential
...
1
2
3
...