Browse by Aston Author

Shen, Yuan, Archambeau, Cédric, Cornford, Dan, Opper, Manfred, Shawe-Taylor, John and Barillec, Remi (2010). A comparison of variational and Markov chain Monte Carlo methods for inference in partially observed stochastic dynamic systems. Journal of Signal Processing Systems, 61 (1), pp. 51-59.

Vrettas, Michail D., Cornford, Dan, Opper, Manfred and Shen, Yuan (2010). A new variational radial basis function approximation for inference in multivariate diffusions. Neurocomputing, 73 (7-9), pp. 1186-1198.

Shen, Y., Cornford, D., Opper, M. and Archambeau, C. (2010). Variational Markov Chain Monte Carlo for Bayesian smoothing of non-linear niffusions. Technical Report. Aston University, Birmingham (UK). (Unpublished)

Olbrich, E., Shen, Yuan, Fukao, K., Meier, P.F. and Wieser, H.G. (2009). Nonlinearity in all-night sleep EEG recorded with foramen ovale electrodes in a patient with temporal lobe epilepsy. Technical Report. Aston University, Birmingham. (Unpublished)

Shen, Yuan, Cornford, Dan and Opper, Manfred (2009). A basis function approach to Bayesian inference in diffusion processes. IN: IEEE/SP 15th Workshop on Statistical Signal Processing, 2009. SSP '09. IEEE.

Vrettas, Michail D., Shen, Yuan and Cornford, Dan (2008). Derivations of variational gaussian process approximation framework. Technical Report. Aston University, Birmingham.

Archambeau, Cédric, Opper, Manfred, Shen, Yuan, Cornford, Dan and Shawe-Taylor, John (2008). Variational inference for diffusion processes. IN: Annual Conference on Neural Information Processing Systems 2007. Platt, J.C.; Koller, D.; Singer, Y. and Roweis, S. (eds) Advances In Neural Information Processing Systems . CAN: MIT.

Shen, Y., Archambeau, C., Cornford, D., Opper, M., Shawe-Taylor, J. and Barillec, R. (2007). Evaluation of variational and Markov Chain Monte Carlo methods for inference in partially observed stochastic dynamic systems. IN: Machine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP. GBR: IEEE.