Concepedia

Graphical Model Structure Learning

Graphical Model Structure Learning is the computational process of inferring the optimal directed acyclic graph structure that represents the conditional dependencies among variables in a graphical model from observed data, distinguishing it from parameter estimation by focusing on the identification of the model's topological framework.

537

Publications

34K

Citations

1.3K

Authors

473

Institutions

Publications per year

2017–2026

157

Parents

Publications

537

The max-min hill-climbing Bayesian network structure learning algorithm

Ioannis Tsamardinos, Laura E. Brown, Constantin Aliferis · Machine Learning · 2006 · 1.8K citations · Full text

, , +9

Learning Bayesian Networks with the<b>bnlearn</b><i>R</i>Package

Marco Scutari · Journal of Statistical Software · 2010 · 1.7K citations · Full text

, , +16

ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation

Di Lin, Jifeng Dai, Jiaya Jia et al. · 2016 · 1.1K citations

, , +14

A Tutorial on Learning with Bayesian Networks

David Heckerman · 1998 · 687 citations · Full text

, , +16

Learning the Structure of Dynamic Probabilistic Networks

Nir Friedman, Kevin J. Murphy, Stuart Russell · arXiv (Cornell University) · 2013 · 574 citations · Full text

, , +12

1–5 of 537

Authors

1.3K

  1. NF
    Nir Friedman

    Hebrew University of Jerusalem, Israel

    9 publications · 1.2K citations

    9

    h-index

  2. AS
    Alan S. Willsky

    Massachusetts Institute of Technology, United States

    8 publications · 932 citations

    8

    h-index

  3. DK
    Daphne Koller

    Stanford University, United States

    8 publications · 1.2K citations

    8

    h-index

  4. PL
    Pedro Larrañaga

    Universidad Politécnica de Madrid, Spain

    7 publications · 629 citations

    7

    h-index

  5. AA
    Animashree Anandkumar

    University of California, Irvine, United States

    7 publications · 204 citations

    7

    h-index

1–5 of 1.3K

Institutions

473

  1. Stanford University

    Stanford, United States

    17 h-index

    33 publications

    3.3K citations

  2. Carnegie Mellon University

    Pittsburgh, United States

    16 h-index

    34 publications

    2.6K citations

  3. Massachusetts Institute of Technology

    Cambridge, United States

    14 h-index

    37 publications

    4.5K citations

  4. University of California, Los Angeles

    Los Angeles, United States

    12 h-index

    30 publications

    3.4K citations

  5. University of Washington

    Seattle, United States

    12 h-index

    33 publications

    1.1K citations

  6. University of Helsinki

    Helsinki, Finland

    8 h-index

    16 publications

    1.7K citations

  7. University of Amsterdam

    Amsterdam, The Netherlands

    8 h-index

    20 publications

    722 citations

  8. ETH Zurich

    Zurich, Switzerland

    8 h-index

    24 publications

    2.9K citations

1–8 of 473

Venues

  1. arXiv (Cornell University)

    Repository · 69 publications · 4.1K citations

    27

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 13 publications · 755 citations

    13

    h-index

  3. International Journal of Approximate Reasoning

    11 publications · 549 citations

    11

    h-index

  4. Information Sciences

    9 publications · 316 citations

    9

    h-index

  5. 9

    h-index

1–5