There are a number of folks both in fishery and the mammal world that use tracking models in Stan for this kind of stuff. So like herehttps://arxiv.org/pdf/1806.10639.pdf

And I don’t remember where I saw the fishery one. If you hunt around for Stan Bayesian animal tracking model or fish tracking (not the right word) that might get ya started.

If you’re willing to discretize (i.e. rasterize) the geographic space, then two useful ecological terms to google might be “step selection function” and “resource selection function”. One popular and useful (albeit non-Bayesian) reference is Turchin’s Quantitative Analysis of Movement Quantitative Analysis of Movement – Peter Turchin

In a simple case, the relative probabilities of different next steps might be based on the inherent suitability (historical use), step length, and the cosine of the turning angle, and then each step can be modeled via a categorical logit regression.

The model I describe above is simple in the sense that the process in this case is assumed to have no memory beyond the previous two locations visited (which together define the turning angle to the any possible next location). But the sky is the limit as you can think about whether/how to incorporate information from earlier steps.

This is just one flavor of approach to this problem, but perhaps you’ll find it useful. The link that @Ara_Winter provides is based on this framework, but builds out additional structure by modeling multiple hidden markov states that correspond to unobserved discrete underlying behavioral modalities for the moving agent.

Thank you for the two nice replies. I had downloaded the paper from Ara’s post it was in my read queue (https://arxiv.org/pdf/1806.10639.pdf). Also, on the discretization, I may get some traction with that approach coupled with the paper. I do have a LSTM RNN that provides reasonable forecast, but I want to develop more of a probability assessment as to the direction.