Computational Bottleneck and potentially uncertainty quantification of G and Sigma

fhui28 / CBFM

Spatio-temporal joint species distribution modeling using community-level basis functions

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Computational Bottleneck and potentially uncertainty quantification of G and Sigma #4

Open fhui28 opened 3 years ago

fhui28 commented 3 years ago

Enough said! Don't really know how to solve this..more like things to think about

fhui28 commented 2 years ago

Also adding uncertainty of G and Sigma. This may be remotely possible since we could calculate the REML/ML wrt with G and Sigma via TMB and get the hessian off of that.

Alternatively, bootstrap to get a poor person's solution?

fhui28 commented 2 years ago

Add parametric structures for Sigma e.g., exponential/Matern covariance with distances dependent on time. This will substantially reduce the number of parameters to estimate, but also according to AZM this tends to work better for temporal forecasting compared to straight up using temporal basis functions...