Low rank continuous-space graphical models

Carl Smith, Frank Wood, Liam Paninski
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1064-1072, 2012.

Abstract

Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven difficult to find general constructions for models in which efficient exact inference is possible, outside of the classical cases of models with restricted graph structure (chain, tree, etc.) and linear-Gaussian or discrete potentials. In this work we identify a tree graphical model class in which exact inference can be performed efficiently, owing to a certain “low-rank” structure in the potentials. We explore this new class of models by applying the resulting inference methods to neural spike rate estimation and motion-capture joint-angle smoothing tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v22-smith12, title = {Low rank continuous-space graphical models}, author = {Smith, Carl and Wood, Frank and Paninski, Liam}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {1064--1072}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/smith12/smith12.pdf}, url = {https://proceedings.mlr.press/v22/smith12.html}, abstract = {Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven difficult to find general constructions for models in which efficient exact inference is possible, outside of the classical cases of models with restricted graph structure (chain, tree, etc.) and linear-Gaussian or discrete potentials. In this work we identify a tree graphical model class in which exact inference can be performed efficiently, owing to a certain “low-rank” structure in the potentials. We explore this new class of models by applying the resulting inference methods to neural spike rate estimation and motion-capture joint-angle smoothing tasks.} }
Endnote
%0 Conference Paper %T Low rank continuous-space graphical models %A Carl Smith %A Frank Wood %A Liam Paninski %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-smith12 %I PMLR %P 1064--1072 %U https://proceedings.mlr.press/v22/smith12.html %V 22 %X Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven difficult to find general constructions for models in which efficient exact inference is possible, outside of the classical cases of models with restricted graph structure (chain, tree, etc.) and linear-Gaussian or discrete potentials. In this work we identify a tree graphical model class in which exact inference can be performed efficiently, owing to a certain “low-rank” structure in the potentials. We explore this new class of models by applying the resulting inference methods to neural spike rate estimation and motion-capture joint-angle smoothing tasks.
RIS
TY - CPAPER TI - Low rank continuous-space graphical models AU - Carl Smith AU - Frank Wood AU - Liam Paninski BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-smith12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 1064 EP - 1072 L1 - http://proceedings.mlr.press/v22/smith12/smith12.pdf UR - https://proceedings.mlr.press/v22/smith12.html AB - Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven difficult to find general constructions for models in which efficient exact inference is possible, outside of the classical cases of models with restricted graph structure (chain, tree, etc.) and linear-Gaussian or discrete potentials. In this work we identify a tree graphical model class in which exact inference can be performed efficiently, owing to a certain “low-rank” structure in the potentials. We explore this new class of models by applying the resulting inference methods to neural spike rate estimation and motion-capture joint-angle smoothing tasks. ER -
APA
Smith, C., Wood, F. & Paninski, L.. (2012). Low rank continuous-space graphical models. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 22:1064-1072 Available from https://proceedings.mlr.press/v22/smith12.html.

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