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Showing 1 to 6 of 6 for “"Bayesian Additive Regression Trees"”.
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Bayesian treed distributed lag models
… dissertation, we develop novel formulations of Bayesian additive regression trees that allow for estimating a DLM. First, we propose treed distributed lag nonlinear models to estimate the association between weekly maternal exposure to air pollution and a birth outcome when the exposure-response …
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Extensions and Applications of Ensemble-of-trees Methods in Machine Learning
Ensemble-of-trees algorithms have emerged to the forefront of machine learning due to their ability to generate high forecasting accuracy for a wide array of regression and classification problems. Classic ensemble methodologies such as random forests (RF) and stochastic gradient boosting (SGB) …
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Essays on Tree-based Methods for Prediction and Causal Inference
… The second chapter explores new variations of Bayesian tree-based machine learning algorithms. Bayesian Additive Regression Trees (BART) (Chipman et al. 2010) and Bayesian Causal Forests (BCF) (Hahn et al. 2020) are state-of-the-art machine learning methods for prediction and causal inference. …
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A Machine Learning Model for Octane Number Prediction
… learning models are data driven with primarily regression and deep learning methods being used in literature as prediction models. This study aims to develop a parsimonious machine learning model which can be used to predict the RON from the molar composition of the gasoline product stream. …
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Trajectories of Change in South Africa's Freshwater Fish Fauna
… improved model accuracy and performance. Using a Bayesian Additive Regression Trees (BART) algorithm, the distributions of three black bass (Micropterus salmoides, M. dolomieu, and M. punctulatus) species and two native species (Clarias gariepinus and Pseudobarbus burgi) were modelled. A total of …
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Survival Prediction For Brain Tumor Patients Using Gene Expression Data
… for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble …