Instructor Notes

Not done yet 😬️. Sorry! Here’s a copy of the Discussion Questions instead.

• What defines a state space model?
• What is the difference between process and observation error?
• What are the advantages of modeling them separately?
• What is the benefit of making predictions of the underlying state even if we can’t observe it?
• What are some of the complexities that can be incorporated into state space models?
• How is the normal dynamic linear model constructed?
• How is nonlinearity integrated?
• Why is fitting state space models challenging?
• How might these models be useful for modeling time-series and making forecasts in ecology