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Undirected probabilistic graphical models represent the conditional dependencies, or Markov properties, of a collection of random variables.
Greedy inference with structure-exploiting lazy maps
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Computing the minimum fill-in is NP-complete
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A note on the delta method
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Graphical models , volume 17
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Simulating normalizing constants: From importance sampling to bridge sampling to path sampling
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A fast and high quality multilevel scheme for partitioning irregular graphs
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A local ensemble kalman filter for atmospheric data assimilation, 2002
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Erich Leo Lehmann · 2004
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Vladimir Igorevich Bogachev, Aleksandr Viktorovich Kolesnikov, and Kirill Vladimirovich Medvedev · 2005
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Causal protein-signaling networks derived from multiparameter single-cell data
Karen Sachs, Omar Perez, Dana Pe’er, Douglas A Lauffenburger, and Garry P Nolan · 2005
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Chinese whispers: an efficient graph clustering algorithm and its application to natural language processing problems
Chris Biemann · 2006
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Covariance matrix selection and estimation via penalised normal likelihood
Jianhua Z Huang, Naiping Liu, Mohsen Pourahmadi, and Linxu Liu · 2006
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High-dimensional graphs and variable selection with the Lasso
Nicolai Meinshausen and Peter Bühlmann · 2006
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High-dimensional graphical model selection using ℓ 1 \ell_{1} -regularized logistic regression
Martin J Wainwright, John D. Lafferty, and Pradeep K. Ravikumar · 2007
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Model selection and estimation in the Gaussian graphical model
Ming Yuan and Yi Lin · 2007
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Model selection through sparse maximum likelihood estimation for multivariate Gaussian or binary data
Onureena Banerjee, Laurent El Ghaoui, and Alexandre d’Aspremont · 2008
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Regularized estimation of large covariance matrices
Peter J Bickel, Elizaveta Levina, et al · 2008
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Timothy A Davis · 2008
The joint graphical lasso for inverse covariance estimation across multiple classes
Patrick Danaher, Pei Wang, and Daniela M Witten · 2014
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Efficiently learning Ising models on arbitrary graphs
Guy Bresler · 2015
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Probabilistic Forecasting and Bayesian Data Assimilation
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Optimal transport for applied mathematicians
Filippo Santambrogio · 2015
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Graphical models via univariate exponential family distributions
Eunho Yang, Pradeep Ravikumar, Genevera I Allen, and Zhandong Liu · 2015
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Wald tests of singular hypotheses
Mathias Drton, Han Xiao, et al · 2016
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Lectures on algebraic statistics , volume 39
Mathias Drton, Bernd Sturmfels, and Seth Sullivant · 2008
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Sparse inverse covariance estimation with the graphical lasso
Jerome Friedman, Trevor Hastie, and Robert Tibshirani · 2008
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Sparse estimation of large covariance matrices via a nested Lasso penalty
Elizaveta Levina, Adam Rothman, Ji Zhu, et al · 2008
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Protein design by sampling an undirected graphical model of residue constraints
John Thomas, Naren Ramakrishnan, and Chris Bailey-Kellogg · 2008
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Optimal Transport: Old and New , volume 338
C. Villani · 2008
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Probabilistic Graphical Models: Principles and Techniques
Daphne Koller and Nir Friedman · 2009
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Estimation of high-dimensional graphical models using regularized score matching
Lina Lin, Mathias Drton, and Ali Shojaie · 2016
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Sampling via measure transport: An introduction
Youssef Marzouk, Tarek Moselhy, Matthew Parno, and Alessio Spantini · 2016
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An unexpected encounter with Cauchy and Lévy
Natesh S Pillai and Xiao-Li Meng · 2016
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Structure learning in graphical modeling
Mathias Drton and Marloes H Maathuis · 2017
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Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting
Rebecca Morrison, Ricardo Baptista, and Youssef Marzouk · 2017
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Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
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The expxorcist: nonparametric graphical models via conditional exponential densities
Arun Suggala, Mladen Kolar, and Pradeep K Ravikumar · 2017
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The Gaussian graphical model in cross-sectional and time-series data
Sacha Epskamp, Lourens J Waldorp, René Mõttus, and Denny Borsboom · 2018
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Transport map accelerated markov chain Monte Carlo
Matthew D Parno and Youssef M Marzouk · 2018
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On nesting Monte Carlo estimators
Tom Rainforth, Rob Cornish, Hongseok Yang, Andrew Warrington, and Frank Wood · 2018
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Learning directed acyclic graph models based on sparsest permutations
Garvesh Raskutti and Caroline Uhler · 2018
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Inference via low-dimensional couplings
Alessio Spantini, Daniele Bigoni, and Youssef Marzouk · 2018
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TransportMaps v2.0
Transport Maps Team · 2018
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Certified dimension reduction in nonlinear Bayesian inverse problems
Olivier Zahm, Tiangang Cui, Kody Law, Alessio Spantini, and Youssef Marzouk · 2018
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A layered multiple importance sampling scheme for focused optimal Bayesian experimental design
Chi Feng and Youssef M Marzouk · 2019
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Sum-of-squares polynomial flow
Priyank Jaini, Kira A Selby, and Yaoliang Yu · 2019
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Computational optimal transport
Gabriel Peyré, Marco Cuturi, et al · 2019
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An improved modified cholesky decomposition approach for precision matrix estimation
Xiaoning Kang and Xinwei Deng · 2020
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On the representation and learning of monotone triangular transport maps
Ricardo Baptista, Olivier Zahm, and Youssef Marzouk · 2022
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Diagonal nonlinear transformations preserve structure in covariance and precision matrices
Rebecca Morrison, Ricardo Baptista, and Estelle Basor · 2022
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Coupling techniques for nonlinear ensemble filtering
Alessio Spantini, Ricardo Baptista, and Youssef Marzouk · 2022
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