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 applications involving sigmoid belief networks, Boltzmann machines and feed-forward neural networks.
|Title of host publication||ICANN 98|
|Subtitle of host publication||Proceedings of the 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998|
|Editors||Lars Niklasson, Mikael Boden, Tom Ziemke|
|Number of pages||10|
|Publication status||Published - 1998|
|Name||Perspectives in Neural Computing|