GraphEM 0.1.6

Gaussian Markov random fields embedded within an EM algorithm

GraphEM

GraphEM refers to the climate field reconstruction approach proposed by Guillot et al. (2015), and its name means Gaussian graphical models embedded within an EM (Expectation-Maximization) algorithm.

Documentation

• Homepage: https://paleopresto.github.io/GraphEM

• Installation: https://paleopresto.github.io/GraphEM/installation.html

• Tutorial (html): https://paleopresto.github.io/GraphEM/tutorial.html

• Tutorial (Jupyter notebooks): https://github.com/paleopresto/GraphEM/tree/master/docsrc/tutorial

Reference of the GraphEM algorithm

• Guillot, D., Rajaratnam, B., & Emile-Geay, J. (2015). Statistical paleoclimate reconstructions via Markov random fields. The Annals of Applied Statistics, 9(1), 324–352. https://doi.org/10.1214/14-AOAS794

Published studies using GraphEM

• Vaccaro, A., Emile-Geay, J., Guillot, D., Verna, R., Morice, C., Kennedy, J., & Rajaratnam, B. (2021). Climate field completion via Markov random fields – Application to the HadCRUT4.6 temperature dataset. Journal of Climate, 1(aop), 1–66. https://doi.org/10.1175/JCLI-D-19-0814.1

• Neukom, R., Steiger, N., Gómez-Navarro, J. J., Wang, J., & Werner, J. P. (2019). No evidence for globally coherent warm and cold periods over the preindustrial Common Era. Nature, 571(7766), 550–554. https://doi.org/10.1038/s41586-019-1401-2

• Wang, Jianghao, Emile-Geay, J., Guillot, D., McKay, N. P., & Rajaratnam, B. (2015). Fragility of reconstructed temperature patterns over the Common Era: Implications for model evaluation. Geophysical Research Letters, 42(17), 7162–7170. https://doi.org/10.1002/2015GL065265

• Wang, J., Emile-Geay, J., Guillot, D., Smerdon, J. E., & Rajaratnam, B. (2014). Evaluating climate field reconstruction techniques using improved emulations of real-world conditions. Clim. Past, 10(1), 1–19. https://doi.org/10.5194/cp-10-1-2014