Corpus ID: 88516842

Optimal Bayesian design for model discrimination via classification.

@inproceedings{Hainy2018OptimalBD,
  title={Optimal Bayesian design for model discrimination via classification.},
  author={Markus Hainy and David J. Price and Olivier Restif and Christopher C. Drovandi},
  year={2018}
}
  • Markus Hainy, David J. Price, +1 author Christopher C. Drovandi
  • Published 2018
  • Mathematics
  • Performing optimal Bayesian design for discriminating between competing models is computationally intensive as it involves estimating posterior model probabilities for thousands of simulated datasets. This issue is compounded further when the likelihood functions for the rival models are computationally expensive. A new approach using supervised classification methods is developed to perform Bayesian optimal model discrimination design. This approach requires considerably fewer simulations from… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 70 REFERENCES

    Reliable ABC model choice via random forests

    VIEW 4 EXCERPTS
    HIGHLY INFLUENTIAL

    The Elements of Statistical Learning

    VIEW 12 EXCERPTS
    HIGHLY INFLUENTIAL

    Classification and Regression Trees

    VIEW 6 EXCERPTS
    HIGHLY INFLUENTIAL

    On a Measure of the Information Provided by an Experiment

    VIEW 11 EXCERPTS
    HIGHLY INFLUENTIAL

    acebayes: Optimal Bayesian Experimental Design using the ACE Algorithm

    • A. M. Overstall, D. C. Woods, M. Adamou
    • R package version 1.5.
    • 2018
    VIEW 10 EXCERPTS
    HIGHLY INFLUENTIAL