Gaussian processes


#1

I presented a livestream, available here, which is meant as part 2 of a series on Bayesian Reinforcement Learning, but this one focuses on Gaussian processes (as a prelude to discussing Gaussian Process Temporal Difference learning in an upcoming presentation).

Of course, the code examples I use are in PyMC3.

Contents:

  1. Quick recap of part 1 (Bayesian Q-Learning and VPI)
  2. Intro to Gaussian processes
  3. Intro to kernel functions
  4. Code example
  5. A few thoughts on good hypotheses and data inference

I hope this will be of interest to someone here.