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Local Derivative-Free Approximation of - Cornell University - people orie cornell

Local Derivative-Free Approximation of - Cornell University - people orie cornell 2025

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The document presents the GRIMA algorithm, a derivative-free method for approximating computationally expensive posterior densities in Bayesian inference. It addresses the challenges of evaluating posterior densities that are costly to compute by focusing on high-probability density (HPD) regions, thereby reducing computational waste. The algorithm iteratively refines its approximation through knot selection and MCMC sampling without relying on derivatives. The effectiveness of GRIMA is demonstrated through simulation experiments and a case study involving the calibration of a nonlinear regression model for watershed management.

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