Creates a solver that evaluates the log-likelihood at random points
and returns the best. Useful for high-dimensional problems where
grid search is infeasible.
Usage
random_search(sampler, n = 100L)
Arguments
- sampler
Function generating random parameter vectors
- n
Number of random points to evaluate
Details
Unlike grid search, random search scales better to high dimensions.
The sampler should generate points in a reasonable region; points
outside the problem's constraint support are skipped.