Comparison of Approximate Methods for Handling Hyperparameters

Comparison of Approximate Methods for Handling Hyperparameters

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by David J.C. MacKay

Venue:	NEURAL COMPUTATION
Citations:	49 - 1 self

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@ARTICLE{MacKay_comparisonof,
    author = {David J.C. MacKay},
    title = {Comparison of Approximate Methods for Handling Hyperparameters},
    journal = {NEURAL COMPUTATION},
    year = {},
    volume = {11},
    pages = {1035--1068}
}

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Abstract

I examine two approximate methods for computational implementation of Bayesian hierarchical models, that is, models which include unknown hyperparameters such as regularization constants and noise levels. In the 'evidence framework' the model parameters are integrated over, and the resulting evidence is maximized over the hyperparameters. The optimized

Citations

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