Gaussian process regression networks

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 599-606

  Wilson, Andrew Gordon ^a   Knowles, David A ^a   Ghahramani, Zoubin ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NEURAL NETWORKS; DATA SETS; GAUSSIAN PROCESS MODELS; GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESSES; HEAVY-TAILED; MULTIPLE OUTPUTS; NOISE CORRELATION; NON-PARAMETRIC; PREDICTIVE DISTRIBUTIONS; REAL DATA SETS; VARIATIONAL BAYES;

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); GENE EXPRESSION; LEARNING SYSTEMS; NEURAL NETWORKS;

REGRESSION ANALYSIS;

EID: 84867133704     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (39)

1. Adams, R.P. and Stegle, O. Gaussian process product models for nonparametric nonstationarity. In ICML, 2008.
2. Alvarez, M.A. and Lawrence. N.D. Computationally efficient convolved multiple output gaussian processes. JMLR. 12:1425-1466, 2011.
3. Bonilla, E., Chai, K.M.A., and Williams, C. Multi-task Gaussian process prediction. In NIPS. 2008.
4. Boyle, P. and Frean, M. Dependent Gaussian processes. In NIPS, 2004.
5. Brooks, C., Burke, S.P., and Persand, G. Benchmarks and the accuracy of GARCH model estimation. International Journal of Forecasting, 17:45-56, 2001.
6. Brownlees, Christian T., Engle, Robert F., and Kelly, Bryan T. A practical guide to volatility forecasting through calm and storm, 2009. Available at SSRN: http://ssrn.com/abstract=1502915.
7. Bru, M.F. Wishart processes. Journal of Theoretical Probability, 4(4):725-751, 1991.
8. Chib, S., Nardari, F., and Shephard, N. Analysis of high dimensional multivariate stochastic volatility models. Journal of Econometrics, 134(2):341-371. 2006. ISSN 0304-4076.
9. Engle, R. New frontiers for ARCTI models. Journal of Applied Econometrics, 17(5):425446, 2002. ISSN 1099-1255.
10. Engle, R.F. and Kroner, K.P. Multivariate simultaneous generalized ARCH. Econometric theory, 11(01):122-150, 1995.
11. Fox, E. and Dunson, D. Bayesian nonparametric covariance regression. Arxiv preprint arXiv:1101.2017, 2011.
12. Friedman, N. and Nachman, I. Gaussian process networks. In UAI, pp. 211-219, 2000.
13. Gelfand, A.E., Schmidt, A.M., Banerjee, S., and Sirmans, CF. Nonstationary multivariate process modeling through spatially varying coregionalization. Test, 13(2): 263-312, 2004. ISSN 1133-0686.
14. Goldberg, Paul W., Williams, Christopher K.I., and Bishop, Christopher M. Regression with input-dependent noise: A Gaussian process treatment. In NIPS, 1998.
15. Goovaerts, P. Geostatistics for natural resources evaluation. Oxford University Press, USA, 1997.
16. Gouriéroux, C., Jasiak, J., and Sufana, R. The Wishart, autoregressive process of multivariate stochastic volatility. Journal of Econometrics, 150(2):167-181, 2009.
17. Hansen, Peter Reinhard and Lunde, Asger. A forecast comparison of volatility models: Does anything beat a GARCH(1,1). Journal of Applied Econometrics, 20(7): 873-889, 2005.
18. Journel, AG. and Huijbregts, CJ. Mining geostatistics. Academic Press (London and New York), 1978.
19. Kersting, K., Plagemann, C., Pfaff, P., and Burgard, W. Most: likely heteroscedastic gaussian process regression. In ICML, 2007.
20. Lázaro-Gredilla, M. and Titsias, M.K. Variational heteroscedastic gaussian process regression. In ICML, 2011.
21. MacKay, David J.C. Introduction to gaussian processes. In Christopher M. Bishop, editor (ed.). Neural Networks and Machine Learning, chapter 1.1, pp. 133-165. Springer-Verlag, 1998.
22. MacKav, D.J.C Bayesian interpolation. Neural Computation, 4(3):415-447, 1992.
23. McCullough, B.D. and Renfro, C.G. Benchmarks and software standards: A case study of GARCH procedures. Journal of Economic and Social Measurement, 25:59-71, 1998.
24. Murray, Iain, Adams, Ryan Prescott, and MacKay, David J.C. Elliptical Slice Sampling. JMLR: W&CP, 9:541-548, 2010.
25. Neal, R.M. Bayesian learning for neural networks. Springer Verlag, 1996. ISBN 0387947248.
26. Poon, Ser-Huang and Granger, Clive W.J. Practical issues in forecasting volatility. Financial Analysts Journal, 61 (1):45-56, 2005.
27. Rasmussen, Carl Edward. Evaluation of Gaussian Processes and Other Methods for Non-linear Regression. PhD thesis, Dept. of Computer Science, University of Toronto, 1996.
28. Rasmussen, Carl Edward and Williams, Christopher K.I. Gaussian processes for Machine Learning. The MIT Press, 2006.
29. Rumelhart, D.E., Hinton, G.E., and Williams, R.J. Learning representations by back-propagating errors. Nature, 323(6088):533-536, 1986.
30. Teh, Y.W., Seeger. M., and Jordan, M.I. Semiparametric latent factor models. In AISTATS, 2005.
31. Tomancak, P., Beaton, A., Weiszmann, R., Kwan, E., Shu, S., Lewis. S.E., et al. Systematic determination of patterns of gene expression during drosophila embryogenesis. Genome Biol, 3(12):0081-0088, 2002.
32. Turner, Richard E. Statistical Models for Natural Sounds. PhD thesis, University College London, 2010.
33. Ver Hoef, J.M. and Barry, R.P. Constructing and fitting models for cokriging and multivariable spatial prediction. Journal of Statistical Planning and Inference. 69(2):275-294, 1998.
34. Wilson, Andrew G and Ghahramani, Zoubin. GPRN supplementary material. 2012. http://mlg.eng.cam.ac.uk/andrew/gprnsupp.pdf.
35. Wilson, Andrew Gordon and Ghahramani, Z. Generalised Wishart Processes. Arxiv preprint arXiv:1101.0240, 2010a.
36. Wilson, Andrew Gordon and. Ghahramani, Z. Generalised Wishart Processes. In UAI, 2011.
37. Wilson, Andrew Gordon and Ghahramani, Zoubin. Copula processes. In NIPS, 2010b.
38. Yu, Byron M, Cunningham, John P, Santhanam, Gopal, Ryu, Stephen I, Shenoy, Krishna V, and Sahani, Maneesh. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. In NIPS, 2009.
39. Zinzen, R.P., Girardot, C., Gagneur, J., Braun, M., and Furlong, E.E.M. Combinatorial binding predicts spatio-temporal cis-regulatory activity. Nature, 462(7269):65-70, 2009.