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Dependency-Structure Learning in Graphical Models

Given a graphical models we, human, can understand the dependency among the random variables pretty well. Also we can compute the distribution of the hidden variables given the observed variables and the structure of the graphical models by existing inference frameworks. However, there are lot of applications in which we don’t know the structure or the topology of the dependency among the random variables. The question is “How can we give the best guess to the structure of the dependency?” So far there have been a lot of works which I’m interested in.

    These are some interesting publications on this topic

    1. Dynamic Trees: A Hierarchical Probabilistic Approach to Image Modelling by Nicholas J. Adams (Ph.D. thesis 2001)
    2. Dynamic Trees by by Christopher K. I. Williams, Nicholas J. Adams (NIPS 1999)
    3. MFDTs: Mean Field Dynamic Trees by Nicholas J Adams, Amos J Storkey, Zoubin Ghahramani, Christopher K I Williams (2000)
    4. Dynamic Trees: A structured Variational Approach Giving Efficient Propagation Rules by Amos Storkey (2000)
    5. Image modeling with position-encoding dynamic trees by by Amos J Storkey, Christopher K I Williams (2003)
    6. Tractable bayesian learning of tree belief networks by Marina Meila, Tommi Jaakkola (2000)
    7. Learning with Mixtures of Trees by Marina Meila, Michael Jordan (JMLR 2000)
    8. Learning with Mixtures of Trees by Marina Meila (Ph.D. thesis 1999)
    9. Estimating dependency structure as a hidden variable by Marina Meila, Michael Jordan (1998)
    10. Being Bayesian About Network Structure: A Bayesian Approach to Structure Discovery in Bayesian Networks by Nir Friedman and Daphne Koller (2003)
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