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This lecture covers the concept of parametric signal models, focusing on AutoRegressive (AR) processes and Markov chains. It explains the definition, synthesis, and analysis of AR processes, as well as the correlation structure and filtering interpretation. The lecture also delves into the generation and correlation analysis of Markov chains, emphasizing linear estimation, prediction, and probability density estimation.