Josset, Laureline, Demyanov, Vasily, Elsheikh, Ahmed H., Lunati, Ivan (2015) Accelerating Monte Carlo Markov chains with proxy and error models. Computers & Geosciences, 85. 38-48 doi:10.1016/j.cageo.2015.07.003

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Reference Type	Journal (article/letter/editorial)
Title	Accelerating Monte Carlo Markov chains with proxy and error models
Journal	Computers & Geosciences
Authors	Josset, Laureline		Author
	Demyanov, Vasily		Author
	Elsheikh, Ahmed H.		Author
	Lunati, Ivan		Author
Year	2015 (December)	Volume	85
Publisher	Elsevier BV
DOI	doi:10.1016/j.cageo.2015.07.003Search in ResearchGate
	Generate Citation Formats
Mindat Ref. ID	454490	Long-form Identifier	mindat:1:5:454490:0
GUID	0
Full Reference	Josset, Laureline, Demyanov, Vasily, Elsheikh, Ahmed H., Lunati, Ivan (2015) Accelerating Monte Carlo Markov chains with proxy and error models. Computers & Geosciences, 85. 38-48 doi:10.1016/j.cageo.2015.07.003
Plain Text	Josset, Laureline, Demyanov, Vasily, Elsheikh, Ahmed H., Lunati, Ivan (2015) Accelerating Monte Carlo Markov chains with proxy and error models. Computers & Geosciences, 85. 38-48 doi:10.1016/j.cageo.2015.07.003
In	(2015) Computers & Geosciences Vol. 85. Elsevier BV

	*Josset, Laureline, Lunati, Ivan (2013) Local and Global Error Models to Improve Uncertainty Quantification. Mathematical Geosciences, 45 (5). 601-620 doi:10.1007/s11004-013-9471-4*
	Sun, Chao, Demyanov, Vasily, Arnold, Daniel (2023) A conditional GAN-based approach to build 3D facies models sequentially upwards. Computers & Geosciences, 181. 105460 doi:10.1016/j.cageo.2023.105460
	Demyanov, V., Backhouse, L., Christie, M. (2015) Geological feature selection in reservoir modelling and history matching with Multiple Kernel Learning. Computers & Geosciences, 85. 16-25 doi:10.1016/j.cageo.2015.07.014
	*Kanevski, M., Demyanov, V. (2015) Statistical learning in geoscience modelling: Novel algorithms and challenging case studies. Computers & Geosciences, 85. 1-2 doi:10.1016/j.cageo.2015.10.007*
	Shishaev, Gleb; Demyanov, Vasily; Arnold, Daniel (2026) History matching under uncertainty of geological scenarios with implicit geological realism control with generative deep learning and graph convolutions. Computers & Geosciences, 214. doi:10.1016/j.cageo.2026.106186
	*Cervone, Guido, Franzese, Pasquale (2010) Monte Carlo source detection of atmospheric emissions and error functions analysis. Computers & Geosciences, 36 (7) 902-909 doi:10.1016/j.cageo.2010.01.007*
	Sartore, Luca, Fabbri, Paolo, Gaetan, Carlo (2016) spMC: an R-package for 3D lithological reconstructions based on spatial Markov chains. Computers & Geosciences, 94. 40-47 doi:10.1016/j.cageo.2016.06.001
	*Gao, Meng, Zhang, Hui (2012) Sequential Monte Carlo methods for parameter estimation in nonlinear state-space models. Computers & Geosciences, 44. 70-77 doi:10.1016/j.cageo.2012.03.013*
	Arnold, Dan, Demyanov, Vasily, Christie, Mike, Bakay, Alexander, Gopa, Konstantin (2016) Optimisation of decision making under uncertainty throughout field lifetime: A fractured reservoir example. Computers & Geosciences, 95. 123-139 doi:10.1016/j.cageo.2016.07.011
	Abdellatif, Alhasan, Elsheikh, Ahmed H., Graham, Gavin, Busby, Daniel, Berthet, Philippe (2022) Generating unrepresented proportions of geological facies using Generative Adversarial Networks. Computers & Geosciences, 162. 105085 doi:10.1016/j.cageo.2022.105085
	Mandolesi, Eric, Ogaya, Xenia, Campanyà, Joan, Piana Agostinetti, Nicola (2018) A reversible-jump Markov chain Monte Carlo algorithm for 1D inversion of magnetotelluric data. Computers & Geosciences, 113. 94-105 doi:10.1016/j.cageo.2018.01.011

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Josset, Laureline, Demyanov, Vasily, Elsheikh, Ahmed H., Lunati, Ivan (2015) Accelerating Monte Carlo Markov chains with proxy and error models. Computers & Geosciences, 85. 38-48 doi:10.1016/j.cageo.2015.07.003

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