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Title:
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An Adaptive Derivative Free Method for Bayesian Posterior Approximation |
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Author:
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Raitoharju, Matti; Ali-Löytty, Simo |
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Abstract:
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In the Gaussian mixture approach a Bayesian posterior probability distribution function is approximated using a weighted sum of Gaussians. This work presents a novel method for generating a Gaussian mixture by splitting the prior taking the direction of maximum nonlinearity into account. The proposed method is computationally feasible and does not require analytical differentiation. Tests show that the method approximates the posterior better with fewer Gaussian components than existing methods. |
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Issue date:
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2012 |
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ISSN:
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1070-9908 |
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Citation:
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Raitoharju, Matti; Ali-Löytty, Simo 2012.
An Adaptive Derivative Free Method for Bayesian Posterior Approximation .
IEEE Signal Processing Letters vol. 19, num. 2, 87-90. |
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DOI:
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http://dx.doi.org/10.1109/LSP.2011.2179800
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Peer review status:
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Peer-reviewed |
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Description:
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© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
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Belongs to:
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IEEE Signal Processing Letters |
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URN:
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http://URN.fi/URN:NBN:fi:tty-201205311164
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Publication type:
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Artikkeli - Article |
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Language:
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en |
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Pages:
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87-90 |
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University:
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Tampereen teknillinen yliopisto - Tampere University of Technology |
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Faculty:
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Luonnontieteiden ja ympäristötekniikan tiedekunta – Faculty of Science and Environmental Engineering |
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Department:
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Matematiikan laitos – Department of Mathematics |
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Copyright:
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This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. |