Bayesian regression is a statistical method that applies Bayes' Theorem to estimate the parameters of a regression model, allowing for the incorporation of prior knowledge along with observed data. This approach results in a distribution of possible parameter values rather than a single point estimate, offering a more nuanced understanding of uncertainty in predictions. By integrating prior beliefs about model parameters and updating them with new data, Bayesian regression facilitates hypothesis testing and model evaluation.
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