Conditional Random Fields in Stan?

Is it a “good idea” to try to “fit” CRMs with Stan ?

I did a google search and got not much info.

It gives me the feeling that it is not such a great idea which makes me wonder why.

In the title you say “CRF” and in the body you say “CRM”, but you haven’t defined either, so it is difficult for anyone to provide help.

:( yes I agree, terribly sorry . It was a typo and more. CRF = conditional random field : https://en.wikipedia.org/wiki/Conditional_random_field

my first guess is that it should be “doable” with Stan (as it is “just” a simple undiricted graphical model, without much hierarchy). My physics intuition tells me that it is very vaguely similar to an Ising model where X is the external magnetic field, and MCMC has been pretty much invented for simulating these kind interacting particle/spin systems.

Just took the liberty to edit the title for clarity. Hope that’s OK.

I’ve fitted a bunch of things recently with sequential dependencies and my small sample experience is that it’s quite easy to end up with a joint probability which is discontinuous and/or not very smooth. This often results in multiple chains failing to converge.

I’m planning to fit a continuous version of the Pott’s model soon for some not-so-small data; so I’ll report how that goes if it’s of interest.

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