The likelihood function is a fundamental concept in statistics that measures the probability of observing the given data under various parameter values of a statistical model. It provides a way to estimate parameters by maximizing this function, indicating how likely the observed data is for different parameter settings. The likelihood function plays a crucial role in both maximum likelihood estimation and Bayesian inference, connecting it to the concepts of prior and posterior distributions.
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