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In this Python code, called MCPostFit, we implement the MonteCarlo Posterior Fit method developed in arXiv:2007.02615.
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adriagova/MCPostFit
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In this Python code, called MCPostFit, we implement the MonteCarlo Posterior Fit method presented in arXiv:2007.02615. Any use of this method requires the corresponding citation of arXiv:2007.02615. Some useful guidelines about how to use the code are provided in the document MCPostFit_user_guidelines.pdf of this repository. We provide, though, a brief summary in the following lines. The compilation of the code has to be carried out as follows, adding seven arguments: python MCPostFit.py arg1 arg2 arg3 arg4 arg5 arg6 arg7 where arg1 = Name and location of the input MCMCM .txt file, i.e. the file containing the Markov chain arg2 = Total number of parameters (cosmological+nuisance) arg3 = Number of lines from the input MCMC file that the user wants to employ in the posterior fit. To use all of them just write "full" arg4 = "ordered" or "random", depending on the way of picking the aforesaid number of lines from the input MCMC file arg5 = Number of parameters employed to compute the cubic and quartic corrections of the fitting posterior distribution. It has to be lower than or equal to arg2 arg6 = Number of sampling points that the user want to generate in the "marginalization" MonteCarlo arg7 = Name and location of the file that contains the covariance matrix employed to perform the jumps in the "marginalization" MonteCarlo Example: compilation of the attached files, in case they are located in the same folder as MCPostFit: python MCPostFit.py Omegak.txt 28 60000 random 17 1000000 Omegak_cov.txt
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In this Python code, called MCPostFit, we implement the MonteCarlo Posterior Fit method developed in arXiv:2007.02615.
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