Bayesian model comparison is a statistical method used to evaluate and compare different models based on their likelihood of explaining observed data, incorporating prior beliefs and evidence. This approach allows for a coherent way to assess multiple hypotheses or models by computing the posterior probabilities, which represent how likely each model is given the data. By balancing prior information with the data at hand, Bayesian model comparison provides a more nuanced perspective on model selection and hypothesis testing.
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