A. Asuncion, P. Smyth, M. Welling, D. Newman, I. Porteous et al., Distributed Gibbs Sampling for Latent Variable Models, In: Scaling up Machine Learning, 2012.
DOI : 10.1017/CBO9781139042918.012

V. Chandola and R. R. Vatsavai, Implementing a gaussian process learning algorithm in mixed parallel environment, Proceedings of the second workshop on Scalable algorithms for large-scale systems, ScalA '11, 2011.
DOI : 10.1145/2133173.2133176

R. F. Chevalier, G. Hoogenboom, R. W. Mcclendon, and J. A. Paz, Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks, Neural Computing and Applications, vol.6, issue.3, pp.151-159, 2011.
DOI : 10.1007/s00521-010-0363-y

D. Eddelbuettel, Seamless R and C++ Integration with Rcpp, 2013.
DOI : 10.1007/978-1-4614-6868-4

A. Jain, R. Mcclendon, G. Hoogenboom, and R. Ramyaa, Prediction of frost for fruit protection using artificial neural networks, American Society of Agricultural Engineers, pp.3-3075, 2003.

R. Neal, Regression and classification using Gaussian process priors, Proceedings of the sixth Valencia international meeting, p.475, 1998.

T. Nguyen and E. Bonilla, Fast allocation of gaussian process experts, Proceedings of The 31st International Conference on Machine Learning, pp.145-153, 2014.

D. Peteiro-barral and B. Guijarro-berdias, A survey of methods for distributed machine learning, Progress in Artificial Intelligence, vol.49, issue.3, pp.1-11, 2013.
DOI : 10.1007/s13748-012-0035-5

L. Ruiz-garcia, L. Lunadei, P. Barreiro, and I. Robla, A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends, Sensors, vol.9, issue.6, pp.4728-4750, 2009.
DOI : 10.3390/s90604728

J. Q. Shi, R. Murray-smith, and D. Titterington, Hierarchical Gaussian process mixtures for regression, Statistics and Computing, vol.12, issue.1, pp.31-41, 2005.
DOI : 10.1007/s11222-005-4787-7

B. A. Smith, G. Hoogenboom, and R. W. Mcclendon, Artificial neural networks for automated year-round temperature prediction, Computers and Electronics in Agriculture, vol.68, issue.1, pp.52-61, 2009.
DOI : 10.1016/j.compag.2009.04.003

B. A. Smith, R. W. Mcclendon, and G. Hoogenboom, Improving air temperature prediction with artificial neural networks, International Journal of Computational Intelligence, vol.3, issue.3, pp.179-186, 2006.

R. L. Snyder and J. De-melo-abreu, Frost forecasting and monitoring. Frost Protection: Fundamentals, Practice, and Economics 1, pp.91-112, 2005.

V. Tresp, Mixtures of Gaussian processes, Advances in Neural Information Processing Systems, pp.654-660, 2001.

N. Wang, N. Zhang, and M. Wang, Wireless sensors in agriculture and food industry???Recent development and future perspective, Computers and Electronics in Agriculture, vol.50, issue.1, pp.1-14, 2006.
DOI : 10.1016/j.compag.2005.09.003

Y. Yang and J. Ma, An Efficient EM Approach to Parameter Learning of the Mixture of Gaussian Processes, Advances in Neural Networks?ISNN 2011, pp.165-174, 2011.
DOI : 10.1162/08997660151134343