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A well known probabilistic method for solving systems of linear algebraic equations (SLAE) is Markov Chain-Monte Carlo (MCMC) method. Some works about this method have focused on estimating the solution vector and inverse matrix of the system as preconditioner. The iterative process, random sampling, compute aarkov Chain with appropriete length, compute the estimators of solutuion represent an exhaustive omputational effor for MCMC method as the size of SLAE increases. Some proposals to tackle the above are serial implementation using an efficient programming language and parallel implementation of MCMC on different parallel architectures. Julia programming language has captured our attention since it has consolidated, relatively fast, as an excellent tool for scientific computation, therefore we have open a new route in our work by implementing the MCMC in Julia v.1.0 in order to test the precision of the method without to paralelize it. This work presents our first iteration over MCMC using this alluring programming language.

Archive DOI: pending