Artículo
Bayesian additive regression trees for probabilistic programming
Quiroga Andiñach, Miriana Esther
; Garay, Pablo Germán
; Alonso, Juan Manuel; Loyola, Juan Martin
; Martín, Osvaldo Antonio
Fecha de publicación:
06/2022
Editorial:
Cornell University
Revista:
arXiv
ISSN:
2331-8422
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Bayesian additive regression trees (BART) is a non-parametric method to approximate functions. It is a black-box method based on the sum of many trees where priors are used to regularize inference, mainly by restricting trees’ learning capacity so that no individual tree is able to explain the data, but rather the sum of trees. We discuss BART in the context of probabilistic programming languages (PPLs), specifically we introduce a BART implementation extending PyMC, a Python library for probabilistic programming. We present a few examples of models that can be built using this probabilistic programming-oriented version of BART, discuss recommendations for sample diagnostics and selection of model hyperparameters, and finally we close with limitations of the current approach and future extensions.
Palabras clave:
BAYESIAN INFERENCE
,
NON-PARAMETRICS
,
PYMC
,
PYTHON
,
BINARY TREES
,
ENSEMBLE METHOD
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Colecciones
Articulos(IMASL)
Articulos de INST. DE MATEMATICA APLICADA DE SAN LUIS
Articulos de INST. DE MATEMATICA APLICADA DE SAN LUIS
Citación
Quiroga Andiñach, Miriana Esther; Garay, Pablo Germán; Alonso, Juan Manuel; Loyola, Juan Martin; Martín, Osvaldo Antonio; Bayesian additive regression trees for probabilistic programming; Cornell University; arXiv; 1; 6-2022; 1-17
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