A Variational Approach to Robust Bayesian Interpolation

Michael E. Tipping, Neil D. Lawrence
:229-238, 2003.

Abstract

This paper details a robust Bayesian interpolation procedure for linear-in-the-parameter models. Robustness is achieved via a Student-$t$ noise model, defined hierarchically in terms of an inverse-Gamma prior distribution over individual Gaussian observation variances. Variational techniques are exploited to update this prior in light of the data, while also inferring all other model variables. The key to this approach is flexibility; it can infer Gaussian noise where appropriate but can adapt to accommodate heavier-tailed distributions in the presence of outliers.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-tipping-variational03, title = {A Variational Approach to Robust Bayesian Interpolation}, author = {Michael E. Tipping and Neil D. Lawrence}, pages = {229--238}, year = {}, editor = {}, url = {http://inverseprobability.com/publications/tipping-variational03.html}, abstract = {This paper details a robust Bayesian interpolation procedure for linear-in-the-parameter models. Robustness is achieved via a Student-$t$ noise model, defined hierarchically in terms of an inverse-Gamma prior distribution over individual Gaussian observation variances. Variational techniques are exploited to update this prior in light of the data, while also inferring all other model variables. The key to this approach is flexibility; it can infer Gaussian noise where appropriate but can adapt to accommodate heavier-tailed distributions in the presence of outliers.} }
Endnote
%0 Conference Paper %T A Variational Approach to Robust Bayesian Interpolation %A Michael E. Tipping %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-tipping-variational03 %I PMLR %J Proceedings of Machine Learning Research %P 229--238 %U http://inverseprobability.com %V %W PMLR %X This paper details a robust Bayesian interpolation procedure for linear-in-the-parameter models. Robustness is achieved via a Student-$t$ noise model, defined hierarchically in terms of an inverse-Gamma prior distribution over individual Gaussian observation variances. Variational techniques are exploited to update this prior in light of the data, while also inferring all other model variables. The key to this approach is flexibility; it can infer Gaussian noise where appropriate but can adapt to accommodate heavier-tailed distributions in the presence of outliers.
RIS
TY - CPAPER TI - A Variational Approach to Robust Bayesian Interpolation AU - Michael E. Tipping AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-tipping-variational03 PB - PMLR SP - 229 DP - PMLR EP - 238 L1 - UR - http://inverseprobability.com/publications/tipping-variational03.html AB - This paper details a robust Bayesian interpolation procedure for linear-in-the-parameter models. Robustness is achieved via a Student-$t$ noise model, defined hierarchically in terms of an inverse-Gamma prior distribution over individual Gaussian observation variances. Variational techniques are exploited to update this prior in light of the data, while also inferring all other model variables. The key to this approach is flexibility; it can infer Gaussian noise where appropriate but can adapt to accommodate heavier-tailed distributions in the presence of outliers. ER -
APA
Tipping, M.E. & Lawrence, N.D.. (). A Variational Approach to Robust Bayesian Interpolation. , in PMLR :229-238

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