Bayesian adaptive dual control of deep brain stimulation in a computational model of Parkinson’s disease
Fig 10
Histograms of the parameters selected by each algorithm in 1D.
Histograms of the parameters selected by each algorithm (rows 2-4) over 1000 trials of 100 function evaluations are show, as well as the underlying response surfaces (top row). Each row shows the sampling patterns of an algorithm as it attempted to minimize beta power in each of the 1D cases (columns). BayesOpt clustered most tightly on the optimum parameter values in all cases. The NM algorithm explored the space the least and was easily trapped in flat regions or in a local minimum. The DIRect algorithm continually explored the space, and never transitioned to exploitation.