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Automatic Differentiation (AD) allows to determine exactly the Taylor series of any function truncated at any order.
S. Linnainmaa, “Taylor expansion of the accumulated rounding error,” BIT Numerical Mathematics
1976
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N. Madras and A. D. Sokal, “The pivot algorithm: A highly efficient monte carlo method for the self-avoiding walk,” Journal of Statistical Physics
1988
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L. Del Debbio, G. M. Manca, and E. Vicari, “Critical slowing down of topological modes,” Phys.Lett
2004
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Society for Industrial and Applied Mathematics, Philadelphia, PA, 2008
A. Griewank, Evaluating derivatives : principles and techniques of algorithmic differentiation · 2008
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PhD thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2012
F. Virotta, Critical slowing down and error analysis of lattice QCD simulations · 2012
Cited alongside, same era.
https://indico.desy.de/indico/event/9420/ . Theory and Practice of data analysis
Lottini, S. and Sommer, R., “Data analysis – lattice practices,” 2014 · 2014
Cited alongside, same era.
Internal notes ALPHA collaboration
Hubert Simma and Rainer Sommer and Francesco Virotta, “General error computation in lattice gauge theory,” 2012-2014 · 2014
Cited alongside, same era.
Cited in the paper.
https://en.wikipedia.org/wiki/Automatic_differentiation . [Online; accessed 22-June-2018]
Wikipedia contributors, “Automatic differentiation — Wikipedia, the free encyclopedia,” 2018 · 2018
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http://pythonhosted.org/uncertainties/ . Uncertainties in python
Lebigot, Eric O., “Uncertainties: a python package for calculations with uncertainties,” 2018 · 2018
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https://gitlab.ift.uam-csic.es/alberto/aderrors
Ramos, Alberto, “ aderrors · 2018
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