  Building upon our earlier work of a martingale approach to global
optimization, a powerful stochastic search scheme for the global optimum of
cost functions is proposed on the basis of change of measures on the states
that evolve as diffusion processes and splitting of the state-space along the
lines of a Bayesian game. To begin with, the efficacy of the optimizer, when
contrasted with one of the most efficient existing schemes, is assessed against
a family of Np-hard benchmark problems. Then, using both simulated- and
experimental data, potentialities of the new proposal are further explored in
the context of an inverse problem of significance in medical imaging, wherein
the superior reconstruction features of a global search vis-\`a-vis the
commonly adopted local or quasi-local schemes are brought into relief.
