An Infinite BART model Article Swipe
Bayesian additive regression trees (BART) are popular Bayesian ensemble models used in regression and classification analysis. Under this modeling framework, the regression function is approximated by an ensemble of decision trees, interpreted as weak learners that capture different features of the data. In this work, we propose a generalization of the BART model that has two main features: first, it automatically selects the number of decision trees using the given data; second, the model allows clusters of observations to have different regression functions since each data point can only use a selection of weak learners, instead of all of them. This model generalization is accomplished by including a binary weight matrix in the conditional distribution of the response variable, which activates only a specific subset of decision trees for each observation. Such a matrix is endowed with an Indian Buffet process prior, and sampled within the MCMC sampler, together with the other BART parameters. We then compare the Infinite BART model with the classic one on simulated and real datasets. Specifically, we provide examples illustrating variable importance, partial dependence and causal estimation.
Related Topics
Concepts
Mathematics
Generalization
Decision tree
Bayesian probability
Artificial intelligence
Model selection
Conditional probability distribution
Regression analysis
Matrix (chemical analysis)
Regression
Tree (set theory)
Regression diagnostic
Conditional probability
Linear regression
Function (biology)
Bayesian inference
Computer science
Bayesian statistics
Ensemble learning
Ensemble forecasting
Segmented regression
Binary decision diagram
Point (geometry)
Feature selection
Bayesian linear regression
Statistics
Linear model
Design matrix
Decision tree model
Polynomial regression
Generalized linear model
Proper linear model
Machine learning
Binary number
Binary data
Decision tree learning
Metadata
- Type
- article
- Landing Page
- http://arxiv.org/abs/2511.20087
- https://arxiv.org/pdf/2511.20087
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7106862012
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7106862012Canonical identifier for this work in OpenAlex
- Title
-
An Infinite BART modelWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-25Full publication date if available
- Authors
-
Battiston, Marco, Luo YuList of authors in order
- Landing page
-
https://arxiv.org/abs/2511.20087Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2511.20087Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2511.20087Direct OA link when available
- Concepts
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Mathematics, Generalization, Decision tree, Bayesian probability, Artificial intelligence, Model selection, Conditional probability distribution, Regression analysis, Matrix (chemical analysis), Regression, Tree (set theory), Regression diagnostic, Conditional probability, Linear regression, Function (biology), Bayesian inference, Computer science, Bayesian statistics, Ensemble learning, Ensemble forecasting, Segmented regression, Binary decision diagram, Point (geometry), Feature selection, Bayesian linear regression, Statistics, Linear model, Design matrix, Decision tree model, Polynomial regression, Generalized linear model, Proper linear model, Machine learning, Binary number, Binary data, Decision tree learningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.using | 67 |
| abstract_inverted_index.which | 119 |
| abstract_inverted_index.work, | 44 |
| abstract_inverted_index.(BART) | 4 |
| abstract_inverted_index.Buffet | 139 |
| abstract_inverted_index.Indian | 138 |
| abstract_inverted_index.allows | 74 |
| abstract_inverted_index.binary | 108 |
| abstract_inverted_index.causal | 180 |
| abstract_inverted_index.first, | 58 |
| abstract_inverted_index.matrix | 110, 133 |
| abstract_inverted_index.models | 9 |
| abstract_inverted_index.number | 63 |
| abstract_inverted_index.prior, | 141 |
| abstract_inverted_index.subset | 124 |
| abstract_inverted_index.trees, | 30 |
| abstract_inverted_index.weight | 109 |
| abstract_inverted_index.within | 144 |
| abstract_inverted_index.capture | 36 |
| abstract_inverted_index.classic | 163 |
| abstract_inverted_index.compare | 156 |
| abstract_inverted_index.endowed | 135 |
| abstract_inverted_index.instead | 95 |
| abstract_inverted_index.partial | 177 |
| abstract_inverted_index.popular | 6 |
| abstract_inverted_index.process | 140 |
| abstract_inverted_index.propose | 46 |
| abstract_inverted_index.provide | 172 |
| abstract_inverted_index.sampled | 143 |
| abstract_inverted_index.second, | 71 |
| abstract_inverted_index.selects | 61 |
| abstract_inverted_index.Bayesian | 0, 7 |
| abstract_inverted_index.Infinite | 158 |
| abstract_inverted_index.additive | 1 |
| abstract_inverted_index.clusters | 75 |
| abstract_inverted_index.decision | 29, 65, 126 |
| abstract_inverted_index.ensemble | 8, 27 |
| abstract_inverted_index.examples | 173 |
| abstract_inverted_index.features | 38 |
| abstract_inverted_index.function | 22 |
| abstract_inverted_index.learners | 34 |
| abstract_inverted_index.modeling | 18 |
| abstract_inverted_index.response | 117 |
| abstract_inverted_index.sampler, | 147 |
| abstract_inverted_index.specific | 123 |
| abstract_inverted_index.together | 148 |
| abstract_inverted_index.variable | 175 |
| abstract_inverted_index.activates | 120 |
| abstract_inverted_index.analysis. | 15 |
| abstract_inverted_index.datasets. | 169 |
| abstract_inverted_index.different | 37, 80 |
| abstract_inverted_index.features: | 57 |
| abstract_inverted_index.functions | 82 |
| abstract_inverted_index.including | 106 |
| abstract_inverted_index.learners, | 94 |
| abstract_inverted_index.selection | 91 |
| abstract_inverted_index.simulated | 166 |
| abstract_inverted_index.variable, | 118 |
| abstract_inverted_index.dependence | 178 |
| abstract_inverted_index.framework, | 19 |
| abstract_inverted_index.regression | 2, 12, 21, 81 |
| abstract_inverted_index.conditional | 113 |
| abstract_inverted_index.estimation. | 181 |
| abstract_inverted_index.importance, | 176 |
| abstract_inverted_index.interpreted | 31 |
| abstract_inverted_index.parameters. | 153 |
| abstract_inverted_index.accomplished | 104 |
| abstract_inverted_index.approximated | 24 |
| abstract_inverted_index.distribution | 114 |
| abstract_inverted_index.illustrating | 174 |
| abstract_inverted_index.observation. | 130 |
| abstract_inverted_index.observations | 77 |
| abstract_inverted_index.Specifically, | 170 |
| abstract_inverted_index.automatically | 60 |
| abstract_inverted_index.classification | 14 |
| abstract_inverted_index.generalization | 48, 102 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.92026023 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |