Convergence diagnostics are methods used to assess whether a statistical model has reached a stable solution during the estimation process, particularly in Bayesian analysis. They play a crucial role in understanding how well the posterior distribution approximates the true parameter values after incorporating prior information. Proper diagnostics help ensure that the inferences drawn from the model are reliable and that the Markov Chain Monte Carlo (MCMC) algorithms used for estimation have mixed well.
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