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Abstract: We compare two methods for approximating the high dimensional integrals encountered in Bayesian neural networks. The frequently used hybrid Monte Carlo method is contrasted with integration by cubature formulae. We combine the cubature integration method with a nonlinear transformation to make the approximation of the integral easier. An experimental comparison of both integration methods for our problem domain is given. (Update)
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BibTeX entry: (Update)
@misc{ wezel-numerical,
author = "M.C. van Wezel and W.A. Kosters",
title = "Numerical Integration by Cubature Formulae in Bayesian Neural Networks",
url = "citeseer.ist.psu.edu/article/vanwezel03numerical.html" }
Citations (may not include all citations):1662 Neural Networks for Pattern Recognition (context) - Bishop - 1995
269 Bayesian learning for neural networks (context) - Neal - 1995
153 A practical Bayesian framework for backpropagation networks (context) - MacKay - 1992 ACM
94 Approximate Calculation of Multiple Integrals (context) - Stroud - 1972
11 Sequential Monte Carlo methods to train neural network model.. (context) - de Freitas, Niranjan et al. - 2000
10 Monomial cubature rules since `Stroud' --- A compilation - Cools, Rabinowitz - 1991
2 Neural networks for intelligent data analysis --- Theoretica.. (context) - van Wezel - 2002
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