, Hpc geophysical simulation test suite, Fitdistr Function in R language

O. Y. Al-jarrah, P. D. Yoo, S. Muhaidat, G. K. Karagiannidis, and K. Taha, Efficient machine learning for big data: A review, Big Data Research, vol.2, issue.3, pp.87-93, 2015.

J. Aldrich, R.a. fisher and the making of maximum likelihood 1912-1922, Statistical Science, vol.12, issue.3, pp.162-176, 1997.

L. V. Ballestra, G. Pacellib, and D. Radi, A very efficient approach to compute the first-passage probability density function in a time-changed brownian model: Applications in finance, Physica A: Statistical Mechanics and its Applications, vol.463, issue.1, pp.330-344, 2016.

R. Belohlávek, B. D. Baets, J. Outrata, and V. Vychodil, Inducing decision trees via concept lattices, Int. Journal of General Systems, vol.38, issue.4, pp.455-467, 2009.

B. Bohn, J. Garcke, R. Iza-teran, A. Paprotny, B. Peherstorfer et al., Analysis of car crash simulation data with nonlinear machine learning methods, Int. Conf. on Computational Science ICCS, pp.621-630, 2013.

R. Campisano, H. Borges, F. Porto, F. Perosi, E. Pacitti et al., Discovering tight space-time sequences, Int. Conf. on Big Data Analytics and Knowledge Discovery, pp.247-257, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01925965

R. Campisano, F. Porto, E. Pacitti, F. Masseglia, and E. S. Ogasawara, Spatial sequential pattern mining for seismic data, Simpósio Brasileiro de Banco de Dados (SBBD), pp.241-246, 2016.

Y. Chalabi and D. Würtz, Flexible distribution modeling with the generalized lambda distribution, pp.2012-2017, 2012.

M. Chen, S. Mao, Y. V. Liu-;-r, G. Coile, M. Balomenos et al., Computationally efficient estimation of the probability density function for the load bearing capacity of concrete columns exposed to fire, Int. Symposium of the Int. Association for Life-Cycle Civil Engineering (IALCCE), vol.19, p.8, 2014.

T. Condie, P. Mineiro, N. Polyzotis, and M. Weimer, Machine learning on big data, 29th IEEE Int. Conf. on Data Engineering, ICDE, pp.1242-1244, 2013.

N. Cressie, Statistics for spatial data, 2015.

J. Dean and S. Ghemawat, Mapreduce: Simplified data processing on large clusters, Symp. on Operating System Design and Implementation (OSDI), pp.137-150, 2004.

J. R. Val, F. Simmross-wattenberg, and C. Alberola-lópez, libstable: Fast, parallel, and high-precision computation of ?-stable distributions in r, c/c++, and matlab, Journal of Statistical Software, vol.78, issue.1, pp.1-25, 2017.

W. J. Dixon and F. J. Massey, Introduction to statistical analysis, 1968.

S. Fotheringham, C. Brunsdon, and M. Charlton, Quantitative Geography: Perspectives on Spatial Data Analysis, 2000.

M. Friedl and C. Brodley, Decision tree classification of land cover from remotely sensed data, Remote Sensing of Environment, vol.61, issue.3, pp.399-409, 1997.

M. Gheisari, G. Wang, and M. Z. Bhuiyan, A survey on deep learning in big data, IEEE Int. Conf. on Computational Science and Engineering, CSE, and IEEE Int. Conf. on Embedded and Ubiquitous Computing, pp.173-180, 2017.

S. Ghemawat, H. Gobioff, and S. Leung, The google file system, ACM Symp. on Operating Systems Principles (SOSP), pp.29-43, 2003.

E. R. Harold, Java I/O: Tips and Techniques for Putting I/O to Work, pp.131-132, 2006.

G. E. Hinton and R. R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science, vol.313, issue.5786, pp.504-507, 2006.

T. J. Jackson, D. M. Vine, A. Y. Hsu, A. Oldak, P. J. Starks et al., Soil moisture mapping at regional scales using microwave radiometry: the southern great plains hydrology experiment, IEEE Transactions Geoscience and Remote Sensing, vol.37, issue.5, pp.2136-2151, 1999.
DOI : 10.1109/36.789610

F. Kathryn, J. T. Oden, and D. Faghihi, A bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems, Journal of Computational Physics, vol.295, pp.189-208, 2015.

T. Kraska, A. Beutel, E. H. Chi, J. Dean, and N. Polyzotis, The case for learned index structures, Int. Conf. on Management of Data (SIGMOD), pp.489-504, 2018.
DOI : 10.1145/3183713.3196909

URL : http://arxiv.org/pdf/1712.01208

S. Landset, T. M. Khoshgoftaar, A. N. Richter, and T. Hasanin, A survey of open source tools for machine learning with big data in the hadoop ecosystem, Journal of Big Data, vol.2, issue.1, p.24, 2015.

J. Liu, E. Pacitti, and P. Valduriez, A survey of scheduling frameworks in big data systems, International Journal of Cloud Computing, p.27, 2018.
URL : https://hal.archives-ouvertes.fr/lirmm-01692229

R. H. Lopes, Kolmogorov-smirnov test, Int. Encyclopedia of Statistical Science, pp.718-720, 2011.

S. Marelli and B. Sudret, UQLab: A Framework for Uncertainty Quantification in MATLAB, 2014.

S. Marelli and B. Sudret, Uqlab: A framework for uncertainty quantification in MATLAB, Int. Conf. on Vulnerability, Risk Analysis and Management (ICVRAM), pp.2554-2563, 2014.
DOI : 10.1061/9780784413609.257

URL : http://www.sudret.ibk.ethz.ch/content/dam/ethz/special-interest/baug/ibk/risk-safety-and-uncertainty-dam/publications/international-conferences/2014_MarelliSudretICVRAM2014.pdf

X. Meng, J. Bradley, B. Yavuz, E. Sparks, S. Venkataraman et al., MLlib: Machine Learning in Apache Spark, Journal of Machine Learning Research, vol.17, issue.34, pp.1-7, 2016.

C. Michele, T. Stefano, and S. Andrea, Sensitivity and uncertainty analysis in spatial modelling based on gis. Agriculture, Ecosystems & Environment, vol.81, issue.1, pp.71-79, 2000.

J. A. Nelder and R. Mead, A simplex method for function minimization, Computer Journal, vol.7, pp.308-313, 1965.
DOI : 10.1093/comjnl/7.4.308

E. E. Prudencio and K. W. Schulz, The parallel C++ statistical library 'queso': Quantification of uncertainty for estimation, simulation and optimization, Euro-Par: Parallel Processing Workshops, pp.398-407, 2011.

J. Ramberg and B. W. Schmeiser, An approximate method for generating asymmetric random variables, Commun. ACM, vol.17, issue.2, pp.78-82, 1974.
DOI : 10.1145/360827.360840

S. R. Safavian and D. Landgrebe, A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man, and Cybernetics, vol.21, issue.3, pp.660-674, 1991.
DOI : 10.1109/21.97458

URL : http://hdl.handle.net/2060/19910014493

R. Sandberg, D. Goldberg, S. Kleiman, D. Walsh, and B. Lyon, Design and implementation of the sun network file system, the Summer USENIX conf, pp.119-130, 1985.

S. Shalev-shwatrz and S. Ben-david, Understanding Machine Learning-From Theory to Algorithms, 2017.

K. Shvachko, H. Kuang, S. Radia, and R. Chansler, The hadoop distributed file system, IEEE Symp. on Mass Storage Systems and Technologies (MSST), pp.1-10, 2010.
DOI : 10.1109/msst.2010.5496972

S. Singer and S. Singer, Complexity analysis of nelder-mead search iterations, Conf. on Applied Mathematics and Computation, pp.185-196, 1999.
DOI : 10.1002/anac.200410015

P. Snyder, tmpfs: A virtual memory file system, European UNIX Users' Group Conf, pp.241-248, 1990.

S. Suthaharan, Big data classification: Problems and challenges in network intrusion prediction with machine learning, ACM SIGMETRICS Performance Evaluation Review, vol.41, issue.4, pp.70-73, 2014.

G. Trajcevski, Uncertainty in spatial trajectories, Computing with Spatial Trajectories, pp.63-107, 2011.

F. Wang and J. Liu, Networked wireless sensor data collection: Issues, challenges, and approaches, IEEE Communications Surveys and Tutorials, vol.13, issue.4, pp.673-687, 2011.

E. D. Karian, Fitting Statistical Distributions: The Generalized Lambda Distribution and Generalized Bootstrap Methods, 2000.

B. Zadrozny and C. Elkan, Obtaining calibrated probability estimates from decision trees and naive bayesian classifiers, Int. Conf. on Machine Learning (ICML), pp.609-616, 2001.

M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, Spark: Cluster computing with working sets, USENIX Workshop on Hot Topics in Cloud Computing (HotCloud), 2010.