New Algorithms for Graphical Models and Their Applications in Learning

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Copyright: Liu, Xianghang
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Abstract
Probabilistic graphical models bring together graph theory and probability theory in a powerful formalism for multivariate statistical modelling. Since many machine learning problems involve the modelling of multivariate probability distributions, graphical mod- els can be a good fit to these problems. In this thesis, we show that applying graphical models in machine learning problems can have several advantages: First, it can better capture the nature of the problem. Second, it gives us great flexibility in modelling. Finally, it provides us with e
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Author(s)
Liu, Xianghang
Supervisor(s)
Caetano, Tiberio
Blair, Alan
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Publication Year
2015
Resource Type
Thesis
Degree Type
PhD Doctorate
UNSW Faculty
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download public version.pdf 2.92 MB Adobe Portable Document Format
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