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Phylogeography and evolutionary dynamics of Nairobi sheep disease virus. [PDF]
Virus EvolMu HZ +9 more
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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.
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Evolutionary Bayesian Rose Trees
IEEE Transactions on Knowledge and Data Engineering, 2015We present an evolutionary multi-branch tree clustering method to model hierarchical topics and their evolutionary patterns over time. The method builds evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which well balances both the fitness of the current tree and ...
Shixia Liu +3 more
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A Bayesian guide to tree felling
Trends in Ecology & Evolution, 2000To most evolutionary biologists, building phylogenetic trees is a bore. Comparative approaches to understanding patterns as diverse as the correlation of body size and brain size in primates, and the evolution of extended phenotypes in gallwasps, require a robust phylogeny on which to place changes in character state.
, Rokas, , McVean
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Tractable Bayesian social learning on trees
2012 IEEE International Symposium on Information Theory Proceedings, 2012We study agents in a social network who learn by observing the actions of their neighbors. The agents iteratively estimate an unknown "state of the world" s from initial private signals, and the past actions of their neighbors in the social network. First, we consider a set of Bayesian agents, and investigate the computational problem the agents face ...
Yashodhan Kanoria, Omer Tamuz
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Bayesian Mapping of Lichens Growing on Trees
Biometrical Journal, 2001Summary: Suitability of trees as hosts for epiphytic lichens are studied in a forest stand of size 25 ha. Suitability is measured as occupation probabilites which are modelled using hierarchical Bayesian approach. These probabilities are useful for an ecologist.
Riiali, Anne +2 more
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Online Learning with Bayesian Classification Trees
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016Randomized classification trees are among the most popular machine learning tools and found successful applications in many areas. Although this classifier was originally designed as offline learning algorithm, there has been an increased interest in the last years to provide an online variant. In this paper, we propose an online learning algorithm for
Bulo, Samuel Rota, Kontschieder, Peter
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On Decision Trees for (1,2)-Bayesian Networks
Fundamenta Informaticae, 2002Bayesian Networks (BN) are convenient tool for representation of probability distribution of variables. We study time complexity of decision trees which compute values of all observable variables from BN. We consider (1,2)-BN in which each node has at most 1 entering edge, and each variable has at most 2 values.
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Applying Bayesian Approach to Decision Tree
2006Applying Bayesian approach to decision tree (DT) model, and then a Bayesian-inference-based decision tree (BDT) model is proposed. For BDT we assign prior to the model parameters. Together with observed samples, prior are converted to posterior through Bayesian inference.
Yatong Zhou, Taiyi Zhang, Zhigang Chen
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