Hi,

I have time series data where the generating process switches back and forth between two types. I’d like to fit a model to the data, and capture the times the switch occurs. I’m thinking a “Gaussian Process Mixture of Experts” would work, where I have two GPs, each modelling one of the two types of data, and a “gating” function that switches between the two.

Here’s some references I’ve found:

- GitHub - aidanscannell/mogpe: Mixtures of Gaussian Process Experts in GPflow/TensorFlow - This seems to do what I want, but in Tensorflow. It uses a GP with Binomial likelihood as the gating function.
- https://proceedings.neurips.cc/paper_files/paper/2001/file/9afefc52942cb83c7c1f14b2139b09ba-Paper.pdf - One of the earlier papers, using Dirichlet process as the gating function.

I had a quick go at using the Pyro GP module, with a GP gating function, but didn’t have much luck. Before going further, I’d like to check if anyone has implemented this in Pyro, or if anyone has any tips?

Any suggestions for alternative ways to model are also welcome.

Thanks!

Paul