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The EM algorithm and the Laplace Approximation
Published 24 Jan 2014 in stat.ML | (1401.6276v1)
Abstract: The Laplace approximation calls for the computation of second derivatives at the likelihood maximum. When the maximum is found by the EM-algorithm, there is a convenient way to compute these derivatives. The likelihood gradient can be obtained from the EM-auxiliary, while the Hessian can be obtained from this gradient with the Pearlmutter trick.
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