A comparison of algorithms for inference and learning in probabilistic graphical models

  title={A comparison of algorithms for inference and learning in probabilistic graphical models},
  author={Brendan J. Frey and Nebojsa Jojic},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
Research into methods for reasoning under uncertainty is currently one of the most exciting areas of artificial intelligence, largely because it has recently become possible to record, store, and process large amounts of data. While impressive achievements have been made in pattern classification problems such as handwritten character recognition, face detection, speaker identification, and prediction of gene function, it is even more exciting that researchers are on the verge of introducing… CONTINUE READING
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Graphical Models

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A modular generative model for layered vision

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Graphical models, variational inference and exponential families

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