Similar to yours above, but in particular i’d like to see more applied examples. Much of the reading i’ve done had gone through examples on how to estimate the model and detailed the mechanics of the model, but there has been less focus on how to convert e.g. the estimated latent states of a hidden markov model into actionable insights to inform a decision.

I think i even have a good use case for these kinds of model in work as i have a big system which has some observable components and proxy variables and risk profiles differ depending on the state of the system… but say i estimate a model which is a good fit to historic data… what are the recipes for abstracting the covariate settings which move the system into riskier states say…

Not sure if i’ve expressed myself well here. But the broad thought is : how do i convert a well estimated state space model into a compelling case for action or decision?

I am mostly familiar with (Extended) Kalman Filter/ Smoother and some simulation based approaches (particle filters/importance sampling), so I’m interested in the algorithms that dynamax uses (Gaussian Hermite KF?)