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Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences.
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An Introduction to Generalized Linear Models
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“A Bayesian Network Meta-Analysis Comparing Concurrent Chemoradiotherapy Followed by Adjuvant Chemotherapy, Concurrent Chemoradiotherapy Alone and Radiotherapy Alone in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.”
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“Learning Directed Acyclic Graphical Structures with Genetical Genomics Data.”
Gao B, Cui Y (2015) · 2015
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“An Analytical Framework for Supply Network Risk Propagation: A Bayesian Network Approach.”
Garvey MD, Carnovale S, Yeniyurt S (2015) · 2015
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“Graphical Models via Univariate Exponential Family Distributions.”
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“Learning Directed Acyclic Graphs with Penalized Neighbourhood Regression.”
Aragam B, Amini AA, Zhou Q (2016) · 2016
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R Core Team (2016) · 2016
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Concave Penalized Estimation of Causal Gaussian Networks with Intervention
Zhang D (2016) · 2016
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Matrix: Sparse and Dense Matrix Classes and Methods
Bates D, Maechler M (2017) · 2017
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“Polyhedral Aspects of Score Equivalence in Bayesian Network Structure Learning.”
Cussens J, Haws D, Studenỳ M (2017) · 2017
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“AMIDST: a Java Toolbox for Scalable Probabilistic Machine Learning.”
Masegosa AR, Martínez AM, Ramos-López D, Cabañas R, Salmerón A, Nielsen TD, Langseth H, Madsen AL (2017) · 2017
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“Penalized Estimation of Directed Acyclic Graphs From Discrete Data.”
Gu J, Fu F, Zhou Q (2018) · 2018
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“Structure Discovery in Bayesian Networks by Sampling Partial Orders.”
Niinimäki T, Parviainen P, Koivisto M (2016) · 2048
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