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This lecture covers the concept of geometric ergodicity in Markov chains, focusing on the convergence properties and ergodic estimators. It discusses the bias and variance of estimators, emphasizing efficiency loss quantification. The presentation concludes with a proof of the variance characterization of TV-norm.