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Paper detail

Modifying iterated Laplace approximations

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.

preprint2015arXivOpen access
Tiep MaiSimon Wilson
MethodologyMachine Learning
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Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
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Tiep MaiSimon Wilson

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