UMA ABORDAGEM BAYESIANA PARA MODELAR A ISOTERMA DE LANGMUIR
Main Article Content
Abstract
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.
Article Details
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).