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The aim of this study was to utilize the Bayesian method for modeling the Langmuir isotherm considering informative and non-informative prior distributions. It was conducted a data simulation study considering different sample sizes to evaluate the precision and accuracy of the estimates of affinity parameter (k) and maximum adsorption capacity (M), where they were obtained with different normal informative priors distribution and not informative uniform distribution, together with the estimates of the parameter _ for which were proposed a Gama informative and uninformative prior distributions. The samples of the marginal posterior distributions of isotherm's parameters were obtained by Gibbs sampler. The inferences were made and the results indicated that the Bayesian method is efficient and the estimates obtained with use of informative prior distributions of the parameters had higher precision and accuracy in the same lower sample sizes. The Langmuir isotherm was modeled with experimental adsorption data considering prior distributions proposals and the results corroborate the simulation study so that the estimates obtained with the informative priors showed higher precision.
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