Bayesian Optimization in Action

6 Using information theory with entropy-based policies

This chapter covers

• Entropy as an information-theoretic measure of uncertainty
• Information gain as a method of reducing entropy
• BayesOpt policies that use information theory for their search

We saw in chapter 4 that by aiming to improve from the best value achieved so far, we can design improvement-based BayesOpt policies, such as Probability of Improvement (POI) and Expected Improvement (EI). In chapter 5, we used multi-armed bandit (MAB) policies to obtain Upper Confidence Bound (UCB) and Thompson sampling (TS), each of which uses a unique heuristic to balance exploration and exploitation in the search for the global optimum of the objective function.

In this chapter, we learn about another ...