Probit Monotone BART
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918132941062144 |
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| author | Fisher, Jared D. |
| author_facet | Fisher, Jared D. |
| contents | Bayesian Additive Regression Trees (BART) of Chipman et al. (2010) has proven to be a powerful tool for nonparametric modeling and prediction. Monotone BART (Chipman et al., 2022) is a recent development that allows BART to be more precise in estimating monotonic functions. We further these developments by proposing probit monotone BART, which allows the monotone BART framework to estimate conditional mean functions when the outcome variable is binary. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00263 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probit Monotone BART Fisher, Jared D. Machine Learning Computation Methodology 62G08 Bayesian Additive Regression Trees (BART) of Chipman et al. (2010) has proven to be a powerful tool for nonparametric modeling and prediction. Monotone BART (Chipman et al., 2022) is a recent development that allows BART to be more precise in estimating monotonic functions. We further these developments by proposing probit monotone BART, which allows the monotone BART framework to estimate conditional mean functions when the outcome variable is binary. |
| title | Probit Monotone BART |
| topic | Machine Learning Computation Methodology 62G08 |
| url | https://arxiv.org/abs/2509.00263 |