Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis

Michael E. Tipping, Neil D. Lawrence
Neurocomputing, 69:123-141, 2005.

Abstract

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 previous termrobustnext term 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.

Cite this Paper

BibTeX
@Article{Tipping-variational05, title = {Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis}, author = {Tipping, Michael E. and Lawrence, Neil D.}, journal = {Neurocomputing}, pages = {123--141}, year = {2005}, volume = {69}, url = {http://inverseprobability.com/publications/tipping-variational05.html}, abstract = {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 previous termrobustnext term 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.} }
Endnote
%0 Journal Article %T Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis %A Michael E. Tipping %A Neil D. Lawrence %J Neurocomputing %D 2005 %F Tipping-variational05 %P 123--141 %U http://inverseprobability.com/publications/tipping-variational05.html %V 69 %X 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 previous termrobustnext term 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.
RIS
TY - JOUR TI - Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis AU - Michael E. Tipping AU - Neil D. Lawrence DA - 2005/01/01 ID - Tipping-variational05 VL - 69 SP - 123 EP - 141 UR - http://inverseprobability.com/publications/tipping-variational05.html AB - 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 previous termrobustnext term 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. ER -
APA
Tipping, M.E. & Lawrence, N.D.. (2005). Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis. Neurocomputing 69:123-141 Available from http://inverseprobability.com/publications/tipping-variational05.html.

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