Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Independent component analysis for identification of artifacts in magnetoencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Neural Computation
Independent component analysis: algorithms and applications
Neural Networks
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Sequential Bayesian kernel modelling with non-Gaussian noise
Neural Networks
Variational Bayesian functional PCA
Computational Statistics & Data Analysis
Mixture of the robust L1 distributions and its applications
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
Variational inference for Student-t MLP models
Neurocomputing
A variational Bayesian method to inverse problems with impulsive noise
Journal of Computational Physics
Robust Gaussian Process Regression with a Student-t Likelihood
The Journal of Machine Learning Research
Dynamic control model of BOF steelmaking process based on ANFIS and robust relevance vector machine
Expert Systems with Applications: An International Journal
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We demonstrate how a variational approximation scheme enables effective inference of key parameters in probabilisitic signal models which employ the Student-t distribution. Using the two scenarios of robust interpolation and independent component analysis (ICA) as examples, we illustrate the key feature of the approach: that the form of the noise distribution in the interpolation case, and the source distributions in the ICA case, can be inferred from the data concurrent with all other model parameters.