Bayesian Inference in Mixtures-of-Experts and Hierarchical Mixtures-of-Experts Models With an Applic
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[1] Radford M. Neal. Sampling from multimodal distributions using tempered transitions , 1996, Stat. Comput..
[2] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[3] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[4] C. Robert,et al. Estimation of Finite Mixture Distributions Through Bayesian Sampling , 1994 .
[5] Michael I. Jordan,et al. Convergence results for the EM approach to mixtures of experts architectures , 1995, Neural Networks.
[6] Radford M. Neal. Bayesian Mixture Modeling by Monte Carlo Simulation , 1991 .
[7] G. E. Peterson,et al. Control Methods Used in a Study of the Vowels , 1951 .
[8] Douglas D. O'Shaughnessy,et al. Speech communication : human and machine , 1987 .
[9] J. Freidman,et al. Multivariate adaptive regression splines , 1991 .
[10] Christopher M. Bishop,et al. Classification and regression , 1997 .
[11] W. Wong,et al. The calculation of posterior distributions by data augmentation , 1987 .
[12] D. Rubin,et al. Inference from Iterative Simulation Using Multiple Sequences , 1992 .
[13] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[14] A. O'Hagan,et al. The Calculation of Posterior Distributions by Data Augmentation: Comment , 1987 .
[15] P. McCullagh,et al. Generalized Linear Models , 1992 .
[16] Biing-Hwang Juang,et al. Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.