Ability to use a generated spatial markov chain to predict the next n states about giddy HOT 6 OPEN

Is this issue fixed. If not Can you tell me how you want to things to be implemented ? I haven't worked with pysal till of now but I have worked in probabilistic graphical models, and currently was working on implementing sampling based on Hamiltonian Monte Carlo. With help I can work this off. Can you suggest starting point specifically code base section that I should go through.

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I've experimented with this before and is currently reorganizing the code to make it complete and convenient to use.

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Looking forward.

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This'll move to giddy & is still an active interest.

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Trying to reconstruct a timeline from memory today for how geosnap came into being, I went looking for this old issue assuming it was over here.

Instead I saw this issue for the first time in years 😂. To close the loop, I've got a simple version of this implemented over in geosnap. It only does a single draw from the spatially-conditioned probs, but should be trivial to wrap in a loop to generate the parameters @stuartlynn wants, no?

Either way, I'd think if that implementation is correct, we should move it here then extend if @ljwolf and @weikang9009 agree?

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(we'd have to generalize the geosnap version for continuous data, obviously, but i mean the core logic should live here, yeah?)

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