Joseph Weston
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 This is not a systematic review of all these fields, but rather, we aim to identify the important traits and design considerations.

 #### Experiment design uses Bayesian sampling because the computational costs are not a limitation.
-Optimal experiment design (OED) is a field of statistics that minimizes the number of experimental runs needed to estimate specific parameters, and thereby, it reduces the costs of experimentation.[@emery1998optimal]
-It works with many degrees of freedom and can consider constraints, for example, when the sample space contains settings that are practically infeasible.
+Optimal experiment design (OED) is a field of statistics that minimizes the number of experimental runs needed to estimate specific parameters and, thereby, reduce the cost of experimentation.[@emery1998optimal]
+It works with many degrees of freedom and can consider constraints, for example when the sample space contains regions that are infeasible for practical reasons.
 One form of OED is response-adaptive design[@hu2006theory], which concerns the adaptive sampling of designs for statistical experiments.
-Here, the acquired data (i.e., the observations) are used to estimate the uncertainties of a certain desired parameter.