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• Intro to Bayes Course (first 2 lessons free)
• Advanced Regression Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

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Takeaways:

• Setting appropriate priors is crucial to avoid overfitting in models.
• R-squared can be used effectively in Bayesian frameworks for model evaluation.
• Dynamic regression can incorporate time-varying coefficients to capture changing relationships.
• Predictively consistent priors enhance model interpretability and performance.
• Identifiability is a challenge in time series models.
• State space models provide structure compared to Gaussian processes.
• Priors influence the model's ability to explain variance.
• Starting with simple models can reveal interesting dynamics.
• Understanding the relationship between states and variance is key.
• State-space models allow for dynamic analysis of time series data.
• AI can enhance the process of prior elicitation in statistical models.

Chapters:

10:09 Understanding State Space Models

14:53 Predictively Consistent Priors

20:02 Dynamic Regression and AR Models

25:08 Inflation Forecasting

50:49 Understanding Time Series Data and Economic Analysis

57:04 Exploring Dynamic Regression Models

01:05:52 The Role of Priors

01:15:36 Future Trends in Probabilistic Programming

01:20:05 Innovations in Bayesian Model Selection

Thank you to my Patrons for making this episode possible!

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