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Global Variational Learning for Graphical Models with Latent Variables
Probabilistic Graphical Models have been used intensively for developing Machine Learning applications including Computer Vision, Natural Language processing, Collaborative Filtering, and Bioinformatics. Moreover, Graphical ...
Parameter Tying and Dissociation in Graphical Models
Understanding the implications of today’s deluge and high velocity of data is a challenging problem facing researchers across multiple disciplines and domains. Data are typically highdimensional, unstructured, and noisy; ...
Variational Inference Methods for Continuous Probabilistic Graphical Models
Graphical models provide a general framework for representing and reasoning about data. Once these models are fit to data, they can be used to answer statistical queries about the observed data. Unfortunately these answering ...