Modifying iterated Laplace approximations
This work addresses incremental improvements to a specific computational method for statistical approximation, with potential benefits for researchers in computational statistics.
The paper tackles the problem of approximation error in the iterLap functional approximation method by introducing several modifications, including adjustments to stopping rules, proposal of a new residual function, starting point selection for optimization, and Hessian matrix scaling, with illustrative examples showing trade-offs between running time and accuracy.
In this paper, several modifications are introduced to the functional approximation method iterLap to reduce the approximation error, including stopping rule adjustment, proposal of new residual function, starting point selection for numerical optimisation, scaling of Hessian matrix. Illustrative examples are also provided to show the trade-off between running time and accuracy of the original and modified methods.