Model comparison is the process of evaluating and contrasting different statistical models to determine which one best explains the observed data. This concept is critical in various aspects of Bayesian analysis, allowing researchers to choose the most appropriate model by considering factors such as prior information, predictive performance, and posterior distributions. By utilizing various criteria like Bayes factors and highest posterior density regions, model comparison aids in decision-making across diverse fields, including social sciences.
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