Hi, I am looking for an expert stan modeler to work with me to speed up a complex model. The model is a time series model using Gaussian Processes. It involves several non-linear transformations of the data and is very complex to fit.

We have tried to parallelize the model, but it appears that this model / data doesn’t lend itself well to parallelization.

- Our gradient evaluation time is ~0.3 seconds
- We are not experiencing post-warmup divergences, but are saturating the treedepth which is leading to long-sampling times and long warmups

Based on the `inv_metric`

, it appears we have parameters on very different scales, which apparently can lead to too-small step sizes. I am trying to reparameterize/rescale, but the parameters in question are strictly positive (e.g. with gamma or exponential priors) and the constrained<>unconstrained space transformations are tough (for me, at least) to reason about in terms of getting the unconstrained parameters onto a unit-variance scale.

Basically the task will be:

- I’ll walk you through the model
- We’ll give you a representative dataset
- We’ll be available to answer questions
- Your goal will be to make the model as efficient as possible
- We’ll pay a competitive hourly rate

If you’re interested, DM me!