Optimal Learning for Stochastic Optimization with Nonlinear Parametric Belief Models

22 Nov 2016Xinyu HeWarren B. Powell

We consider the problem of estimating the expected value of information (the knowledge gradient) for Bayesian learning problems where the belief model is nonlinear in the parameters. Our goal is to maximize some metric, while simultaneously learning the unknown parameters of the nonlinear belief model, by guiding a sequential experimentation process which is expensive... (read more)

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