Optimal Learning
نویسندگان
چکیده
Optimal learning addresses the problem of efficiently collecting information with which to make decisions. These problems arise in both offline settings (making a series of measurements, after which a decision is made) and online settings (the process of making a decision results in observations that change the distribution of belief about future observations). Optimal learning is an issue primarily in applications where observations or measurements are expensive. These include expensive simulations (where a single observation might take a day or more), laboratory sciences (testing a drug compound in a lab), and field experiments (testing a new energy saving technology in a building). This tutorial provides an introduction to this problem area, covering important dimensions of a learning problem and introducing a range of policies for collecting information.
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