Results 11 to 20 of about 1,530,540 (307)
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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Tree models: a Bayesian perspective [PDF]
, 2006 Submitted in partial fulfilment of the requirements for the degree of Master of Philosophy at Queen Mary, University of London, November 2006Classical tree models represent an attempt to create nonparametric models which have good predictive powers as ...
Egan, Blaise
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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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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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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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CoRR, 2012 Hierarchical structure is ubiquitous in data across many domains. There are many hierarchical clustering methods, frequently used by domain experts, which strive to discover this structure. However, most of these methods limit discoverable hierarchies to those with binary branching structure.
Blundell, C, Teh, Y, Heller, K
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MRBAYES: Bayesian inference of phylogenetic trees [PDF]
Bioinformatics, 2001 Abstract Summary: The program MRBAYES performs Bayesian inference of phylogeny using a variant of Markov chain Monte Carlo. Availability: MRBAYES, including the source code, documentation, sample data files, and an executable, is available at http://brahms.biology.rochester.edu/software.html.
John P. Huelsenbeck, Fredrik Ronquist
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Bayesian tree-structured image modeling [PDF]
4th IEEE Southwest Symposium on Image Analysis and Interpretation, 2002 Wavelet-domain hidden Markov models have proven to be useful tools for statistical signal and image processing. The hidden Markov tree (HMT) model captures the key features of the joint statistics of the wavelet coefficients of real-world data. One potential drawback to the HMT framework is the need for computationally expensive iterative training ...
Justin K. Romberg +2 more
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Bayesian Learning of Markov Network Structure [PDF]
, 2006 We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend naive Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar ...
Rish, Irina +3 more
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Mitochondrial DNA. Part B. Resources, 2018 In this study, the complete mitogenome of the blackmouth croaker Atrobucca nibe was obtained. Its mitogenome is 16,842 bp in length, consisting of 37 genes with the typical gene order and direction of transcription in vertebrates.
Wei-Di Yang +3 more
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