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

Learning Bayesian Statistics

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State Space Models and the Kalman Filter

This chapter explores the practicality of state space models and their integration with Gaussian processes, emphasizing their user-friendly nature in making predictions. It highlights the Kalman filter's historical significance and its role in forecasting, as well as how it manages measurement errors with dynamic data updates. Additionally, the chapter discusses advanced techniques in time series analysis, including particle filtering and the challenges of non-Gaussian distributions.

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