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Variational Learning in Graphical Models and Neural Networks
[chapter]
1998
ICANN 98
Variational methods are becoming increasingly popular for inference and learning in probabilistic models. By providing bounds on quantities of interest, they offer a more controlled approximation framework than techniques such as Laplace's method, while avoiding the mixing and convergence issues of Markov chain Monte Carlo methods, or the possible computational intractability of exact algorithms. In this paper we review the underlying framework of variational methods and discuss example
doi:10.1007/978-1-4471-1599-1_2
fatcat:wwba75whkneo7fvdaf75xvuu44