r-acebayes 1.10 Optimal Bayesian experimental design using the ACE algorithm

Finding an optimal Bayesian experimental design involves maximizing an objective function given by the expectation of some appropriately chosen utility function with respect to the joint distribution of unknown quantities (including responses). This objective function is usually not available in closed form and the design space can be continuous and of high dimensionality. This package uses Approximate Coordinate Exchange (ACE) to maximise an approximation to the expectation of the utility function.