Relaxometry Models Compared with Bayesian Technique: Ganglioside Micelle Example
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In this work a methodology to perform Bayesian model-comparison is developed and exemplified in the analysis of magnetic relaxation dispersion (NMRD) experiments of water in Ganglioside micelle system. The NMRD powerful probe of slow dynamics in complex liquids is obtained. There are many interesting systems to study with NMRD, such as ionic and Lyotropic liquids or electrolytes. However, to progress in the understanding of the physical chemistry of studied systems relatively detailed theoretical NMRD-models are required. To improve the models they need to be carefully compared, in addition to physico-chemical considerations of molecular and spin dynamics. The comparison is performed by computing the Bayesian evidence in terms of a thermodynamic integral solved with Markov chain Monte Carlo. The result leads to a conclusion of two micelle water sites, and rules out lower and higher complexity level, i.e., one and three sites. In contrast, and provided only with the quality of best fit, suggest a three site model. The two approximate selection tools, Akaike and Baysian information criterions, may lead to wrong conclusions compared to the the full integration. The methodology is expected to be one of several important tools in NMRD model development, however, is completely general and should find awakened use in many research areas.