Join us for a session on “Implementing GPTs: Separating Inference from Model”
Speaker: Daniel Lee, Thomas Wiecki, PhD
Time: 16:00 UTC/9 am PT/12 pm ET/6 pm Berlin
Location: Online, via Zoom
Register here: Implementing GPTs in Probabilistic Programming: Separating Inference from Model, Thu, Aug 10, 2023, 12:00 PM | Meetup
This will be a high-level talk discussing the separation of statistical models and inference algorithms.
Things we’d like to talk about:
- The general vernacular combines two concepts together: model + inference. But they can be thought of separately.
- Given a statistical model, there are (at least) 3 different types of inference. Optimization, approximate inference, Bayesian inference. We’ll talk about some of the use cases of each. And where stochastic optimization fits in.
- A description of GPTs and how it can be implemented in Stan (and similarly in PyMC or any other PPL).
This talk won’t be overly technical. The goal will be to try to solidify the differences between the different types of inference and when to apply them. There will be plenty of time for Q&A.