A repo include example of paper, A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning.
Given facts:
- 0.2 ≻ 0.1
- 0.35 ≻ 0.5
- 0.2 ≻ 0.35
- 0.2 ≻ 0.6
- 0.8 ≻ 0.7
and pose a gaussian process prior, how can we infer about the hidden function?
See demonstration.ipynb
for detail.
While the original paper use Laplace approximation, this implementation use HMC with Stan/PyStan to do inference.