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Discussion of: "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" [PDF]
, 2020 Contributed discussion included in P. Richard Hahn. Jared S. Murray. Carlos M. Carvalho. "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion)." Bayesian Anal.
Parnell, Andrew C +6 more
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Mitochondrial DNA. Part B. Resources, 2017 In this study, the complete mitogenome of the tigertooth croaker Otolithes ruber was first determined. This mitogenome is 16,589 bp in length, and consists of 37 genes with the typical gene order and direction of transcription in vertebrates. The overall
Chang-Chang Guo +3 more
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Fault diagnosis of mine drainage system based on fuzzy Bayesian network
Gong-kuang zidonghua, 2022 The mine drainage system is developing towards automation and intelligence. The system's structure and function are becoming more and more complex, and the abnormal function and failure of a single component may cause the failure of the whole system. The
SHI Xiaojuan, YAO Bing, GU Huabei
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Quantile pyramids for Bayesian nonparametrics [PDF]
, 2009 Polya trees fix partitions and use random probabilities in order to construct random probability measures. With quantile pyramids we instead fix probabilities and use random partitions.
Hjort, Nils Lid, Walker, Stephen G.
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Remote Sensing, 2022 Individual-tree aboveground biomass (AGB) estimation is vital for precision forestry and still worth exploring using multi-platform LiDAR data for high accuracy and efficiency.
Man Wang +3 more
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Tree Exploration for Bayesian RL Exploration [PDF]
2008 International Conference on Computational Intelligence for Modelling Control & Automation, 2008 Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, where optimality improves with increased computational time. This is because the resulting planning task takes the form of a dynamic programming problem on a belief tree with ...
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Bayesian learning of a tree substitution grammar [PDF]
Proceedings of the ACL-IJCNLP 2009 Conference Short Papers on - ACL-IJCNLP '09, 2009 Tree substitution grammars (TSGs) offer many advantages over context-free grammars (CFGs), but are hard to learn. Past approaches have resorted to heuristics. In this paper, we learn a TSG using Gibbs sampling with a nonparametric prior to control subtree size.
Matt Post, Daniel Gildea
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A Bayesian model for gene family evolution
BMC Bioinformatics, 2011 Background A birth and death process is frequently used for modeling the size of a gene family that may vary along the branches of a phylogenetic tree. Under the birth and death model, maximum likelihood methods have been developed to estimate the birth ...
Liu Liang +3 more
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Explanation Trees for Causal Bayesian Networks
CoRR, 2008 Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)
Ulf H. Nielsen +2 more
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Bayesian Nonparametric Inference for a Multivariate Copula Function [PDF]
, 2014 The paper presents a general Bayesian nonparametric approach for estimating a high dimensional copula. We first introduce the skew-normal copula, which we then extend to an infinite mixture model.
Wu, Juan, Wang, Xue, Walker, Stephen G.
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