dynaTree: Dynamic trees for learning and design Inference by sequential Monte Carlo for dynamic tree regression and classification models with hooks provided for sequential design and optimization, fully online learning with drift, variable selection, and sensitivity analysis of inputs. Illustrative examples from the original dynamic trees paper are facilitated by demos in the package; see demo(package=”dynaTree”)
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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
- Gramacy, Robert B.; Taddy, Matt; Wild, Stefan M.: Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning (2013)
- Bilionis, Ilias; Zabaras, Nicholas: Multi-output local Gaussian process regression: applications to uncertainty quantification (2012)
- Lopes, Hedibert F.; Tsay, Ruey S.: Particle filters and Bayesian inference in financial econometrics (2011)