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#124 State Space Models & Structural Time Series, with Jesse Grabowski

Learning Bayesian Statistics

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Understanding State Space Models in Time Series Analysis

This chapter explores the intricacies of state space models as a structured alternative to Gaussian processes in time series analysis. It highlights key concepts such as recursion and innovations, discussing their implications for decision-making and forecasting accuracy. The narrative also examines the balance between model complexity and practical effectiveness, using the Gaussian random walk as a foundational example.

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