Efficient Sampling for Gaussian Process Inference using Control Variables

Michalis K. TitsiasNeil D. LawrenceMagnus Rattray
,  21:1681-1688, 2009.

Abstract

Sampling functions in Gaussian process (GP) models is challenging because of the highly correlated posterior distribution. We describe an efficient Markov chain Monte Carlo algorithm for sampling from the posterior process of the GP model. This algorithm uses control variables which are auxiliary function values that provide a low dimensional representation of the function. At each iteration, the algorithm proposes new values for the control variables and generates the function from the conditional GP prior. The control variable input locations are found by continuously minimizing an objective function. We demonstrate the algorithm on regression and classification problems and we use it to estimate the parameters of a differential equation model of gene regulation.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-titsias-efficient08, title = {Efficient Sampling for Gaussian Process Inference using Control Variables}, author = {Michalis K. Titsias and Neil D. Lawrence and Magnus Rattray}, pages = {1681--1688}, year = {}, editor = {}, volume = {21}, address = {Cambridge, MA}, url = {http://inverseprobability.com/publications/titsias-efficient08.html}, abstract = {Sampling functions in Gaussian process (GP) models is challenging because of the highly correlated posterior distribution. We describe an efficient Markov chain Monte Carlo algorithm for sampling from the posterior process of the GP model. This algorithm uses control variables which are auxiliary function values that provide a low dimensional representation of the function. At each iteration, the algorithm proposes new values for the control variables and generates the function from the conditional GP prior. The control variable input locations are found by continuously minimizing an objective function. We demonstrate the algorithm on regression and classification problems and we use it to estimate the parameters of a differential equation model of gene regulation.} }
Endnote
%0 Conference Paper %T Efficient Sampling for Gaussian Process Inference using Control Variables %A Michalis K. Titsias %A Neil D. Lawrence %A Magnus Rattray %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-titsias-efficient08 %I PMLR %J Proceedings of Machine Learning Research %P 1681--1688 %U http://inverseprobability.com %V %W PMLR %X Sampling functions in Gaussian process (GP) models is challenging because of the highly correlated posterior distribution. We describe an efficient Markov chain Monte Carlo algorithm for sampling from the posterior process of the GP model. This algorithm uses control variables which are auxiliary function values that provide a low dimensional representation of the function. At each iteration, the algorithm proposes new values for the control variables and generates the function from the conditional GP prior. The control variable input locations are found by continuously minimizing an objective function. We demonstrate the algorithm on regression and classification problems and we use it to estimate the parameters of a differential equation model of gene regulation.
RIS
TY - CPAPER TI - Efficient Sampling for Gaussian Process Inference using Control Variables AU - Michalis K. Titsias AU - Neil D. Lawrence AU - Magnus Rattray BT - PY - DA - ED - ID - pmlr-v-titsias-efficient08 PB - PMLR SP - 1681 DP - PMLR EP - 1688 L1 - UR - http://inverseprobability.com/publications/titsias-efficient08.html AB - Sampling functions in Gaussian process (GP) models is challenging because of the highly correlated posterior distribution. We describe an efficient Markov chain Monte Carlo algorithm for sampling from the posterior process of the GP model. This algorithm uses control variables which are auxiliary function values that provide a low dimensional representation of the function. At each iteration, the algorithm proposes new values for the control variables and generates the function from the conditional GP prior. The control variable input locations are found by continuously minimizing an objective function. We demonstrate the algorithm on regression and classification problems and we use it to estimate the parameters of a differential equation model of gene regulation. ER -
APA
Titsias, M.K., Lawrence, N.D. & Rattray, M.. (). Efficient Sampling for Gaussian Process Inference using Control Variables. , in PMLR :1681-1688

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