Example of A Tutorial on Bayesian Optimization

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.

References

• Brochu, Eric, Vlad M. Cora, and Nando De Freitas. "A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning." arXiv preprint arXiv:1012.2599 (2010).