Dynamic Trees for Learning and Design

作者：Taddy Matthew A^*; Gramacy Robert B; Polson Nicholas G

来源：Journal of the American Statistical Association, 2011, 106(493): 109-123.

DOI：10.1198/jasa.2011.ap09769

• Active learning
• BART
• CART
• Gaussian process
• Nonparametric regression
• Online classification
• Particle learning
• Partition tree
• Search optimization
• Sequential design of experiments
• TGP

Dynamic regression trees are an attractive option for automatic regression and classification with complicated response surfaces in online application settings. We create a sequential tree model whose state changes in time with the accumulation of new data, and provide particle learning algorithms that allow for the efficient online posterior filtering of tree states. A major advantage of tree regression is that it allows for the use of very simple models within each partition. The model also facilitates a natural division of labor in our sequential particle-based inference: tree dynamics are defined through a few potential changes that are local to each newly arrived observation, while global uncertainty is captured by the ensemble of particles. We consider both constant and linear mean functions at the tree leaves, along with multinomial leaves for classification problems, and propose default prior specifications that allow for prediction to be integrated over all model parameters conditional on a given tree. Inference is illustrated in some standard nonparametric regression examples, as well as in the setting of sequential experiment design, including both active learning and optimization applications, and in online classification. We detail implementation guidelines and problem specific methodology for each of these motivating applications. Throughout, it is demonstrated that our practical approach is able to provide better results compared to commonly used methods at a fraction of the cost.

• 出版日期2011-3