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Dynamic Trees for Learning and Design

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  • Taddy, Matthew A.
  • Gramacy, Robert B.
  • Polson, Nicholas G.

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  • Taddy, Matthew A. & Gramacy, Robert B. & Polson, Nicholas G., 2011. "Dynamic Trees for Learning and Design," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 109-123.
    • Handle: RePEc:bes:jnlasa:v:106:i:493:y:2011:p:109-123
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    Cited by:

    1. Charles Audet & Sébastien Le Digabel & Renaud Saltet, 2022. "Quantifying uncertainty with ensembles of surrogates for blackbox optimization," Computational Optimization and Applications, Springer, vol. 83(1), pages 29-66, September.
    2. Oyebayo Ridwan Olaniran & Ali Rashash R. Alzahrani, 2023. "On the Oracle Properties of Bayesian Random Forest for Sparse High-Dimensional Gaussian Regression," Mathematics, MDPI, vol. 11(24), pages 1-29, December.
    3. Javier Pórtoles & Camino González & Javier M. Moguerza, 2018. "Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach," Energies, MDPI, vol. 11(6), pages 1-21, June.
    4. Ruimeng Hu, 2019. "Deep Learning for Ranking Response Surfaces with Applications to Optimal Stopping Problems," Papers 1901.03478, arXiv.org, revised Mar 2020.
    5. Moulin, Thibault & Perasso, Antoine & Gillet, François, 2018. "Modelling vegetation dynamics in managed grasslands: Responses to drivers depend on species richness," Ecological Modelling, Elsevier, vol. 374(C), pages 22-36.
    6. Yi Liu & Veronika Ročková & Yuexi Wang, 2021. "Variable selection with ABC Bayesian forests," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 453-481, July.
    7. Jaouad Mourtada & Stéphane Gaïffas & Erwan Scornet, 2021. "AMF: Aggregated Mondrian forests for online learning," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 505-533, July.
    8. Charles Audet & Michael Kokkolaras & Sébastien Le Digabel & Bastien Talgorn, 2018. "Order-based error for managing ensembles of surrogates in mesh adaptive direct search," Journal of Global Optimization, Springer, vol. 70(3), pages 645-675, March.
    9. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
    10. Wei-Yin Loh, 2014. "Fifty Years of Classification and Regression Trees," International Statistical Review, International Statistical Institute, vol. 82(3), pages 329-348, December.
    11. Waley W. J. Liang & Herbert K. H. Lee, 2019. "Bayesian nonstationary Gaussian process models via treed process convolutions," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 797-818, September.

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