I tried to find texts, journal articles, blog posts, etc., to understand how Bayesian estimation (for example,
NUTS) works for ODE parameter estimation, but I could not. None of the material I found on the web deal with parameter estimation for systems of ODEs.
For instance, Turing’s website provides an excellent tutorial using the Lotka-Volterra model as to how to implement the framework for inferring parameters of an ODE model. More specifically, using the tutorial, what do these lines mean?
# Observations. for i in 1:length(predicted) data[:, i] ~ MvNormal(predicted[i], σ^2 * I) end
How is the process of ‘inferring the actual parameter values’ executed? How are the data simulated from solving the ODEs compared with the actual observed data, and what is estimated for such ‘comparison’?
Could someone post links or names of such material in this thread?