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

Michael E. TippingNeil D. Lawrence
,  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
@InProceedings{pmlr-v-tipping-variational05, title = {Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis}, author = {Michael E. Tipping and Neil D. Lawrence}, pages = {123--141}, year = {}, editor = {}, 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 Conference Paper %T Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis %A Michael E. Tipping %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-tipping-variational05 %I PMLR %J Proceedings of Machine Learning Research %P 123--141 %U http://inverseprobability.com %V %W PMLR %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 - CPAPER TI - Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis AU - Michael E. Tipping AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-tipping-variational05 PB - PMLR SP - 123 DP - PMLR EP - 141 L1 - 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.. (). Variational inference for Student-$t$ models: Robust Bayesian interpolation and generalised component analysis. , in PMLR :123-141

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