Posterior odds refer to the ratio of the probabilities of a hypothesis being true versus it being false after considering new evidence. This concept is crucial in Bayesian inference, as it helps to update beliefs based on observed data, integrating prior probabilities and the likelihood of the data given those probabilities. The posterior odds provide a framework for decision-making under uncertainty and are fundamental in statistical modeling and hypothesis testing.
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