BibTeX
@ARTICLE{
         Ramos2019Adf,
       title = "Automatic differentiation for error analysis of {M}onte {C}arlo data",
       journal = "Computer Physics Communications",
       volume = "238",
       pages = "19--35",
       year = "2019",
       issn = "0010-4655",
       doi = "10.1016/j.cpc.2018.12.020",
       url = "https://www.sciencedirect.com/science/article/pii/S0010465519300013",
       author = "Alberto Ramos",
       keywords = "Lattice QCD, Monte Carlo, Error analysis",
       abstract = "Automatic Differentiation (AD) allows to determine exactly the Taylor series of any
         function truncated at any order. Here we propose to use AD techniques for Monte Carlo data analysis.
         We discuss how to estimate errors of a general function of measured observables in different Monte
         Carlo simulations. Our proposal combines the Γ-method with Automatic differentiation,
         allowing exact error propagation in arbitrary observables, even those defined via iterative
         algorithms. The case of special interest where we estimate the error in fit parameters is discussed
         in detail. We also present a freely available fortran reference implementation of the ideas
         discussed in this work.",
       ad_area = "Error Analysis",
       ad_tools = "aderrors"
}