On Input Selection with Reversible Jump Markov Chain Monte Carlo Sampling
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[1] J. Davenport. Editor , 1960 .
[2] B. Ripley,et al. Pattern Recognition , 1968, Nature.
[3] Josef Kittler,et al. Pattern recognition : a statistical approach , 1982 .
[4] Donald Geman,et al. Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[5] Editors , 1986, Brain Research Bulletin.
[6] Hans G. C. Tråvén,et al. A neural network approach to statistical pattern classification by 'semiparametric' estimation of probability density functions , 1991, IEEE Trans. Neural Networks.
[7] Michael I. Jordan,et al. Supervised learning from incomplete data via an EM approach , 1993, NIPS.
[8] Michael I. Miller,et al. REPRESENTATIONS OF KNOWLEDGE IN COMPLEX SYSTEMS , 1994 .
[9] Terrence J. Sejnowski,et al. A Mixture Model System for Medical and Machine Diagnosis , 1994, NIPS.
[10] B. Carlin,et al. Bayesian Model Choice Via Markov Chain Monte Carlo Methods , 1995 .
[11] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .
[12] Walter R. Gilks,et al. Bayesian model comparison via jump diffusions , 1995 .
[13] Geoffrey E. Hinton,et al. Bayesian Learning for Neural Networks , 1995 .
[14] Sylvia Richardson,et al. Markov Chain Monte Carlo in Practice , 1997 .
[15] P. Green,et al. Corrigendum: On Bayesian analysis of mixtures with an unknown number of components , 1997 .
[16] C. C. Homes,et al. Bayesian Radial Basis Functions of Variable Dimension , 1998, Neural Computation.