Optimizing parameter constraints: a new tool for Fisher matrix forecasts
  In a Bayesian context, theoretical parameters are correlated random
variables. Then, the constraints on one parameter can be improved by either
measuring this parameter more precisely - or by measuring the other parameters
more precisely. Especially in the case of many parameters, a lengthy process of
guesswork is then needed to determine the most efficient way to improve one
parameter's constraints. In this short article, we highlight an extremely
simple analytical expression that replaces the guesswork and that facilitates a
deeper understanding of optimization with interdependent parameters.
