Bayesian Neural Networks and Density Networks (1994)

by David J.C. MacKay
Venue:Nuclear Instruments and Methods in Physics Research, A
Citations:40 - 8 self

Active Bibliography

7 Density Networks and their Application to Protein Modelling – David J.C. MacKay - 1996
David J.C. MacKay and Mark N. Gibbs – Cavendish Laboratory Madingley, David J. C. Mackay, Mark N. Gibbs - 1997
67 Comparison of Approximate Methods for Handling Hyperparameters – David J.C. MacKay
1 Divide and Conquer: Pattern Recognition using Mixtures of Experts – Steve Waterhouse, Steve Waterhouse - 1997
18 Hyperparameters: optimize, or integrate out? – David J. C. MacKay - 1996
13 Bayesian Methods for Neural Networks: Theory and Applications – David J.C. MacKay, Cavendish Laboratory - 1995
50 Bayesian Non-linear Modelling for the Prediction Competition – David J.C. MacKay, Cavendish Laboratory - 1994
1 Bayesian Regularisation Methods In A Hybrid Mlp--Hmm System – Steve Renals And, Steve Renals, David Mackayy - 1993
Bayesian parameter estimation via variational methods – unknown authors - 1998
105 Bayesian Parameter Estimation Via Variational Methods – Tommi S. Jaakkola , Michael I. Jordan - 1999
The Problem with Quantitative Studies of International Conflict – Nathaniel Beck, Gary King, Langche Zeng - 1998
18 Regression with Gaussian Processes – Christopher K. I. Williams - 1995
218 Gaussian Processes for Regression – Christopher K. I. Williams, Carl Edward Rasmussen - 1996
University of Cambridge. – unknown authors
3 Recognition in hierarchical models – Peter Dayan - 1997
4 Recurrent Sampling Models for the Helmholtz Machine – Peter Dayan - 1999
24 Factor analysis using delta-rule wake-sleep learning – Radford M. Neal, Peter Dayan - 1997
193 The Helmholtz Machine – Peter Dayan, Geoffrey E. Hinton, Radford M. Neal, Richard S. Zemel - 1995
2 Variational inference for continuous sigmoidal Bayesian networks – Brendan J. Frey - 1996