-
2
-
-
0005495142
-
-
Statistical design theory and Bayesian analysis, Springer series in statistics, 2nd ed., Springer
-
(1985)
-
-
Berger, J.O.1
-
3
-
-
0001141391
-
On the development of reference priors
-
Bernardo J.M., Berger J.O., Dawid A.P., Smith A.F.M. (Eds.), Bayesian statistics 4, Oxford University Press
-
(1992)
, pp. 35-60
-
-
Berger, J.O.1
Bernardo, J.M.2
-
6
-
-
0005483106
-
-
Neural networks for pattern recognition, Oxford University Press
-
(1995)
-
-
Bishop, C.M.1
-
7
-
-
84898994542
-
Regression with input-dependent noise: a Bayesian treatment
-
Mozer M.C., Jordan M.I., Petsche T. (Eds.), Advances in neural information processing systems 9, MIT Press
-
(1997)
, pp. 347-353
-
-
Bishop, C.M.1
Qazaz, C.S.2
-
12
-
-
0000259511
-
Approximate statistical tests for comparing supervised classification learning algorithms
-
(1998)
Neural Computation
, vol.10
, Issue.7
, pp. 1895-1924
-
-
Dietterich, T.G.1
-
15
-
-
0001803816
-
Model determination using sampling-based methods
-
Gilks W.R., Richardson S., Spiegelhalter D.J. (Eds.), Markov chain Monte Carlo in practice, Chapman & Hall
-
(1996)
, pp. 145-162
-
-
Gelfand, A.E.1
-
16
-
-
0001582213
-
Inference and monitoring convergence
-
Gilks W.R., Richardson S., Spiegelhalter D.J. (Eds.), Markov chain Monte Carlo in practice, Chapman & Hall
-
(1996)
, pp. 131-144
-
-
Gelman, A.1
-
20
-
-
0005440451
-
-
Gilks W.R., Richardson S., Spiegelhalter D.J. (Eds.), Markov chain Monte Carlo in practice, Chapman & Hall
-
(1996)
-
-
-
22
-
-
77956890234
-
Monte Carlo sampling methods using Markov chains and their applications
-
(1970)
Biometrika
, vol.57
, Issue.1
, pp. 97-109
-
-
Hastings, W.K.1
-
23
-
-
0005458525
-
Empirical evaluation of Bayesian sampling for neural classifiers
-
Niklasson L., Boden M., Ziemke T. (Eds.), ICANN '98: Proceedings of the Eighth International Conference on Artificial Neural Networks, Springer
-
(1998)
-
-
Husmeier, D.1
Penny, W.D.2
Roberts, S.J.3
-
24
-
-
0005408961
-
-
Theory of probability, 3rd ed., Oxford University Press
-
(1961)
-
-
Jeffreys, J.1
-
28
-
-
85054435084
-
Neural network ensembles, cross-validation, and active learning
-
Tesauro G., Touretzky D.S., Leen T.K. (Eds.), Advances in neural information processing systems 7, MIT Press
-
(1995)
, pp. 231-238
-
-
Krogh, A.1
Vedelsby, J.2
-
31
-
-
0005416567
-
-
Lemm, J. C. (1996). Prior information and generalized questions. Technical report AIM 1598, CBCLP 141, Massachusetts Institute of Technology, Artificial Intelligence Laboratory and Center for Biological and Computational Learning, Department of Brain and Cognitive Sciences.
-
-
-
-
32
-
-
0005419248
-
-
Lemm, J. C. (1999). Bayesian field theory. Technical report MS-TP1-99-1, Universität Münster, Institut für Theoretische Physik.
-
-
-
-
37
-
-
0005467571
-
-
Neal, R. M. (1992). Bayesian training of backpropagation networks by the hybrid Monte Carlo method. Technical report CRG-TR-92-1, Department of Computer Science, University of Toronto.
-
-
-
-
38
-
-
0005511912
-
-
volume 118 of Lecture notes in statistics, Springer-Verlag
-
(1996)
-
-
Neal, R.M.1
-
40
-
-
0002628667
-
Regression and classification using Gaussian process priors (with discussion)
-
Bernardo J.M., Berger J.O., Dawid A.P., Smith A.F. (Eds.), Bayesian statistics 6, Oxford University Press
-
(1999)
, pp. 475-501
-
-
Neal, R.M.1
-
42
-
-
0005446489
-
-
Rasmussen, C. E. (1996). Evaluation of Gaussian processes and other methods for non-linear regression. PhD thesis, Department of Computer Science, University of Toronto.
-
-
-
-
44
-
-
0005499021
-
-
Sarle, W. S. (1997). How to measure importance of inputs? [online]. Technical report, SAS Institute Inc., Cary, NC, USA. Revised 23 June 2000. Available at: ftp://ftp.sas.com/pub/neural/importance.html
-
-
-
-
48
-
-
0005498738
-
-
Vehtari, A. & Lampinen, J. (1999). Bayesian neural networks with correlating residuals. In IJCNN '99: Proceedings of the 1999 International Joint Conference on Neural Networks [CD-ROM], number 2061. IEEE.
-
-
-
-
49
-
-
0005419978
-
-
Vehtari, A. & Lampinen, J. (2000). On Bayesian model assessment and choice using cross-validation predictive densities. Technical report B23, Laboratory of Computational Engineering, Helsinki University of Technology.
-
-
-
-
50
-
-
0005458526
-
-
Vehtari, A., Heikkonen, J., Lampinen, J. & Juujärvi, J. (1998). Using Bayesian neural networks to classify forest scenes. In D. P. Casasent (Ed.), Intelligent robots and computer vision XVII: algorithms, techniques, and active vision, volume 3522 of Proceedings of SPIE (pp. 66-73). SPIE.
-
-
-
-
51
-
-
0005483108
-
-
Vehtari, A., Särkkä, S. & Lampinen, J. (2000). On MCMC sampling in Bayesian MLP neural networks. In S.-I. Amari, C. L. Giles, M. Gori & V. Piuri (Eds.), IJCNN '2000: Proceedings of the 2000 International Joint Conference on Neural Networks, volume 1 (pp. 317-322). IEEE.
-
-
-
-
52
-
-
0005505745
-
-
Vivarelli, F. & Williams, C. K. I. (1997). Using Bayesian neural networks to classify segmented images. In Proceedings of the IEE Fifth International Conference on Artificial Neural Networks, number 40 in Conference Publications (pp. 268-263). The Institution of Electrical Engineers.
-
-
-
-
53
-
-
0005466825
-
-
Winther, O. (1998). Bayesian mean field algorithms for neural networks and Gaussian processes. PhD thesis, University of Copenhagen.
-
-
-
-
56
-
-
0005408963
-
-
Wolpert, D. H. & Macready, W. G. (1995). No free lunch theorems for search. Technical report SFI-TR-95-02-010. The Santa Fe Institute.
-
-
-
-
57
-
-
0005409887
-
-
Yang, R. & Berger, J. O. (1997). A catalog of noninformative priors. ISDS Discussion paper 97-42, Institute of Statistics and Decision Sciences, Duke University.
-
-
-
|