Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks | Paper Citation Record

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This paper cites Oxford: Oxford University Press; 1996.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Oxford: Oxford University Press; 1996

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This paper cites Cambridge: Cambridge University Press; 2000.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Cambridge: Cambridge University Press; 2000

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This paper cites Being Bayesian about network structure.Mach Learn.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Being Bayesian about network structure.Mach Learn

Reference 3

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This paper cites Causal protein-signaling networks derived from multiparameter single-cell data.Science.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Causal protein-signaling networks derived from multiparameter single-cell data.Science

Reference 4

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This paper cites In:BiomedicalImageProcessingandBiomedicalVisualization.Proc.SPIE1905.1993:861-870.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks In:BiomedicalImageProcessingandBiomedicalVisualization.Proc.SPIE1905.1993:861-870

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This paper cites New York: Academic Press; 1973:239-273.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks New York: Academic Press; 1973:239-273

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This paper cites A Bayesian method for the induction of probabilistic networks from data.Mach Learn.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks A Bayesian method for the induction of probabilistic networks from data.Mach Learn

Reference 7

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This paper cites Parameter priors for directed acyclic graphical models and the charac- terization of several probability distributions.Ann Statist.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Parameter priors for directed acyclic graphical models and the charac- terization of several probability distributions.Ann Statist

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This paper cites Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach.Stat Med.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Learning Markov equivalence classes of directed acyclic graphs: an objective Bayes approach.Stat Med

Reference 9

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This paper cites Posterior graph selection and estimation consistency for high- dimensional Bayesian DAG models.Ann Statist.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Posterior graph selection and estimation consistency for high- dimensional Bayesian DAG models.Ann Statist

Reference 11

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This paper cites BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs

Reference 12

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This paper cites Wishart distributions: Advances in theory with Bayesian applicationJournal of Multivariate Analysis.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Wishart distributions: Advances in theory with Bayesian applicationJournal of Multivariate Analysis

Reference 13

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This paper cites DAGs with NO TEARS: continuous optimization for structure learning.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks DAGs with NO TEARS: continuous optimization for structure learning

Reference 14

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This paper cites In:Advances in Neural Information Processing Systems; 2020:17943-17954.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks In:Advances in Neural Information Processing Systems; 2020:17943-17954

Reference 15

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This paper cites In:Advances in Neural Information Processing Systems; 2022.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks In:Advances in Neural Information Processing Systems; 2022

Reference 16

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Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks DAGs with no curl: an efficient DAG structure learning approach

Reference 17

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This paper cites Truncated matrix power iteration for differentiable DAG learning.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Truncated matrix power iteration for differentiable DAG learning

Reference 18

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This paper cites TriOpt: A Scalable Algorithm for Linear Causal Discovery.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks TriOpt: A Scalable Algorithm for Linear Causal Discovery

Reference 19

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This paper cites Learning directed acyclic graphs via bootstrap aggregating.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Learning directed acyclic graphs via bootstrap aggregating

Reference 20

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This paper cites DAGBagM: learning directed acyclic graphs of mixed vari- ables with an application to identify protein biomarkers for treatment response in ovarian cancer.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks DAGBagM: learning directed acyclic graphs of mixed vari- ables with an application to identify protein biomarkers for treatment response in ovarian cancer

Reference 21

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This paper cites Optimal structure identification with greedy search.J Mach Learn Res.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Optimal structure identification with greedy search.J Mach Learn Res

Reference 22

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Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks A transformational characterization of equivalent Bayesian network structures

Reference 23

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Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Estimating high-dimensional directed acyclic graphs with the PC- algorithm.J Mach Learn Res

Reference 24

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This paper cites Identifiability of Gaussian structural equation models with equal error variances.Biometrika.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Identifiability of Gaussian structural equation models with equal error variances.Biometrika

Reference 25

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Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks 2009;71(2):319-392

Reference 26

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Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks NAZARIET AL 31

Reference 27

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Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Lecture Notes in Statistics, vol

Reference 28

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This paper cites Generating generalized inverse Gaussian random variates.Stat Comput.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Generating generalized inverse Gaussian random variates.Stat Comput

Reference 29

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This paper cites Strong time dependence of the 76-gene prognostic signature for node-negativebreastcancerpatientsintheTRANSBIGmulticenterindependentvalidationseries.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Strong time dependence of the 76-gene prognostic signature for node-negativebreastcancerpatientsintheTRANSBIGmulticenterindependentvalidationseries

Reference 30

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This paper cites Gene expression profiling in breast cancer: understanding the molecularbasisofhistologicgradetoimproveprognosis.JNatlCancerInst.2006;98(4):262-272.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Gene expression profiling in breast cancer: understanding the molecularbasisofhistologicgradetoimproveprognosis.JNatlCancerInst.2006;98(4):262-272

Reference 31

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This paper cites BeingBayesian aboutnetwork structure:aBayesian approachto structure discovery in Bayesian networks.Mach Learn.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks BeingBayesian aboutnetwork structure:aBayesian approachto structure discovery in Bayesian networks.Mach Learn

Reference 32

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This paper cites 2004;5:549-573.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks 2004;5:549-573

Reference 33

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This paper cites Partition MCMC for inference on acyclic digraphs.J Am Stat Assoc.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Partition MCMC for inference on acyclic digraphs.J Am Stat Assoc

Reference 34

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This paper cites Addendum on the scoring of Gaussian directed acyclic graphical models.Ann Statist.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Addendum on the scoring of Gaussian directed acyclic graphical models.Ann Statist

Reference 35

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This paper cites 2019;47(6):3413-3437.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks 2019;47(6):3413-3437

Reference 36

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This paper cites Lasso meets horseshoe: a survey.Statist Sci.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Lasso meets horseshoe: a survey.Statist Sci

Reference 37

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This paper cites High dimensional Bayesian inference for Gaussian directed acyclic graph models.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks High dimensional Bayesian inference for Gaussian directed acyclic graph models

Reference 38

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This paper cites Interleukin-8 in breast cancer progression.J Interferon Cytokine Res.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Interleukin-8 in breast cancer progression.J Interferon Cytokine Res

Reference 39

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This paper cites Recent advances reveal IL-8 signaling as a potential key to targeting breast cancer stem cells.Breast Cancer Res.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Recent advances reveal IL-8 signaling as a potential key to targeting breast cancer stem cells.Breast Cancer Res

Reference 40

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This paper cites 2014;8(7):1278-1289.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks 2014;8(7):1278-1289

Reference 41

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This paper cites Prognostic characterization of OAS1/OAS2/OAS3/OASL in breast cancer.BMC Cancer.

Scalable Bayesian structure learning of directed acyclic graphs via Laplace approximation, with an application to breast cancer gene expression networks Prognostic characterization of OAS1/OAS2/OAS3/OASL in breast cancer.BMC Cancer

Reference 42

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