Ensemble learning in Bayesian neural networks
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[1] A. M. Walker. On the Asymptotic Behaviour of Posterior Distributions , 1969 .
[2] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[3] James O. Berger,et al. Statistical Decision Theory and Bayesian Analysis, Second Edition , 1985 .
[4] A. Kennedy,et al. Hybrid Monte Carlo , 1987 .
[5] J. Berger. Statistical Decision Theory and Bayesian Analysis , 1988 .
[6] Chris Bishop,et al. Current address: Microsoft Research, , 2022 .
[7] David J. C. MacKay,et al. A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.
[8] Geoffrey E. Hinton,et al. Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.
[9] Radford M. Neal. A new view of the EM algorithm that justifies incremental and other variants , 1993 .
[10] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[11] Barak A. Pearlmutter. Fast Exact Multiplication by the Hessian , 1994, Neural Computation.
[12] David Mackay,et al. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .
[13] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[14] David Barber,et al. Ensemble Learning for Multi-Layer Networks , 1997, NIPS.
[15] Neil D. Lawrence,et al. Approximating Posterior Distributions in Belief Networks Using Mixtures , 1997, NIPS.
[16] David Barber,et al. Radial Basis Functions: A Bayesian Treatment , 1997, NIPS.
[17] Christopher M. Bishop. Variational Learning in Graphical Models and Neural Networks , 1998 .
[18] Michael I. Jordan. Learning in Graphical Models , 1999, NATO ASI Series.
[19] David Barber,et al. Tractable Undirected Approximations for Graphical Models , 1998 .
[20] Neil D. Lawrence,et al. Mixture Representations for Inference and Learning in Boltzmann Machines , 1998, UAI.