Latent force models

Journal of Machine Learning Research

Volumn 5, Issue , 2009, Pages 9-16

  Alvarez, Mauricio ^a   Luengo, David ^b   Lawrence, Neil D ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

^b UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL BIOLOGY; DATA DRIVEN MODELLING; DATA-DRIVEN APPROACH; FORCE MODEL; GAUSSIAN PROCESSES; GEO-STATISTICS; HYBRID APPROACH; KERNEL FUNCTION; MOTION CAPTURE; PHYSICAL MODEL; UNDERLYING SYSTEMS;

BIOINFORMATICS; DIFFERENTIAL EQUATIONS;

ARTIFICIAL INTELLIGENCE;

EID: 80053151379     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (11)

1. Mauricio Alvarez and Neil D. Lawrence. Sparse convolved gaussian processes for multi-output regression. In Advances in Neural Information Processing Systems 21, pages 57-64. MIT Press, 2009.
2. Martino Barenco, Daniela Tomescu, Daniel Brewer, Robin Callard, Jaroslav Stark, and Michael Hubank. Ranked prediction of p53 targets using hidden variable dynamic modeling. Genome Biology, 7(3):R25, 2006.
3. Phillip Boyle and Marcus Frean. Dependent Gaussian processes. In Lawrence Saul, Yair Weiss, and Léon Bouttou, editors, Advances in Neural Information Processing Systems, volume 17, pages 217-224, Cambridge, MA, 2005. MIT Press.
4. Pei Gao, Antti Honkela, Magnus Rattray, and Neil D. Lawrence. Gaussian process modelling of latent chemical species: Applications to inferring transcription factor activities. Bioinformatics, 24:i70-i75, 2008.
5. Pierre Goovaerts. Geostatistics For Natural Resources Evaluation. Oxford University Press, 1997.
6. David M. Higdon. Space and space-time modelling using process convolutions. In C. Anderson, V. Barnett, P. Chatwin, and A. El-Shaarawi, editors, Quantitative methods for current environmental issues, pages 37-56. Springer-Verlag, 2002.
7. Neil D. Lawrence, Guido Sanguinetti, and Magnus Rattray. Modelling transcriptional regulation using Gaussian processes. In Bernhard Schölkopf, John C. Platt, and Thomas Hofmann, editors, Advances in Neural Information Processing Systems, volume 19, pages 785-792, Cambridge, MA, 2007. MIT Press.
8. Neil D. Lawrence. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. Journal of Machine Learning Research, 6:1783-1816, Nov. 2005.
9. Andrei D. Polyanin. Handbook of Linear Partial Differential Equations for Engineers and Scientists. Chapman & Hall/CRC Press, 2002.
10. Joaquin Quin~onero Candela and Carl Edward Rasmussen. A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6:1939-1959, 2005.
11. Yee Whye Teh, Matthias Seeger, and Michael I. Jordan. Semiparametric latent factor models. In Robert G. Cowell and Zoubin Ghahramani, editors, Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, pages 333-340, Barbados, 6-8 January 2005. Society for Artificial Intelligence and Statistics.