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An Explainable Bayesian Decision Tree Algorithm [PDF]
Frontiers in Applied Mathematics and Statistics, 2021 Bayesian Decision Trees provide a probabilistic framework that reduces the instability of Decision Trees while maintaining their explainability. While Markov Chain Monte Carlo methods are typically used to construct Bayesian Decision Trees, here we ...
Giuseppe Nuti +2 more
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Enhanced machine learning tree classifiers for lithology identification using Bayesian optimization
Applied Computing and Geosciences, 2022 Lithology identification is a fundamental activity in oil and gas exploration. The application of artificial intelligence (AI) is currently being adopted as a state-of-the-art means of automating lithology identification.
Solomon Asante-Okyere +2 more
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Bayesian additive regression trees with model trees [PDF]
Statistics and Computing, 2021 Bayesian Additive Regression Trees (BART) is a tree-based machine learning method that has been successfully applied to regression and classification problems. BART assumes regularisation priors on a set of trees that work as weak learners and is very flexible for predicting in the presence of non-linearity and high-order interactions.
Estevão B. Prado +2 more
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Barekeng, 2023 Bayesian Additive Regression Tree (BART) is a sum-of-trees model used to approximate classification or regression cases. The main idea of this method is to use a prior distribution to keep the tree size small and a likelihood from data to get the ...
Stevanny Budiana +2 more
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Students' learning style detection using tree augmented naive Bayes [PDF]
Royal Society Open Science, 2018 Students are characterized according to their own distinct learning styles. Discovering students' learning style is significant in the educational system in order to provide adaptivity.
Ling Xiao Li, Siti Soraya Abdul Rahman
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Bayesian Additive Regression Trees using Bayesian model averaging [PDF]
Statistics and Computing, 2017 Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for data sets where the number of variables $p$ is large (e.g.
Belinda Hernández +3 more
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Mitochondrial DNA. Part B. Resources, 2020 In this study, the complete mitogenome of a new species, Johnius taiwanensis (Chao et al. ) was obtained. Its mitogenome is 18,451 bp in length, consisting of 37 genes with the typical gene order and direction of transcription in vertebrates.
Bai-an Lin +4 more
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Different Visualizations Cause Different Strategies When Dealing With Bayesian Situations
Frontiers in Psychology, 2020 People often struggle with Bayesian reasoning. However, previous research showed that people’s performance (and rationality) can be supported by the way the statistical information is represented.
Andreas Eichler +2 more
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Bayesian Learning with Mixtures of Trees [PDF]
, 2006 We present a Bayesian method for learning mixtures of graphical models. In particular, we focus on data clustering with a tree-structured model for each cluster. We use a Markov chain Monte Carlo method to draw a sample of clusterings, while the likelihood of a clustering is computed by exact averaging over the model class, including the dependency ...
Jussi Kollin, Mikko Koivisto
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Sharded Bayesian Additive Regression Trees
CoRR, 2023 In this paper we develop the randomized Sharded Bayesian Additive Regression Trees (SBT) model. We introduce a randomization auxiliary variable and a sharding tree to decide partitioning of data, and fit each partition component to a sub-model using Bayesian Additive Regression Tree (BART).
Hengrui Luo, Matthew T. Pratola
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