Decision Procedure

• Make assumptions about function characteristics to evaluate possible test points. Pick optimal evaluation point.

• Global optimization for each decision?

  • No. Too expensive computationally.

• Sampling decision space

  • Limited computational expense
  • Approximates optimal decision

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In actuality, we avoid the computational complexity of dealing with complex probability distributions by encoding our assumptions in a function which ranks possible points to evaluate.

However, even so, there's still a tradeoff: The computational cost of optimizing our decision procedure can outweigh its benefit. We therefore limit our computational cost by sampling the decision space, thus approximating an optimal decision.