This paper is available on arxiv under CC 4.0 license. Authors: Vitor da Fonseca, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa; Tiago Barreiro, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa and 2ECEO, Universidade Lus´ofona; Nelson J. Nunes, Instituto de Astrof´ısica e Ciˆencias do Espa¸co, Faculdade de Ciˆencias da Universidade de Lisboa.
Methodology The likelihoods are minimized using the Monte Carlo code of the MontePython parameter estimation package , which samples the parameter space and the posterior probability distributions using a Metropolis-Hastings algorithm with the flat priors specified in Table I. The size of the prior intervals is sufficient for the corresponding posteriors to fall exponentially within them, except for the parameter β as we will discuss later. B.
Methodology The likelihoods are minimized using the Monte Carlo code of the MontePython parameter estimation package , which samples the parameter space and the posterior probability distributions using a Metropolis-Hastings algorithm with the flat priors specified in Table I. The size of the prior intervals is sufficient for the corresponding posteriors to fall exponentially within them, except for the parameter β as we will discuss later. B.
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