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| Presentation | 2009-03-11 16:10 Numerical Calculation of Stochastic Complexties through Optimization of Gaussian Mixture centered on MCMC Samples Takayuki Higo, Kenji Nagata, Sumio Watanabe (Tokyo Inst. of Tech.)  NC2008-112 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Stochastic complexity is a criterion for model selection and determination of hyper parameters in Bayesian learning．If samples following the posterior distribution are obtained, however it is not easy to calculate its stochastic complexity. Therefore, conventional manners calculate the posterior distribution several times with changing the temperature parameter through Markov chain Monte Carlo method (MCMC). Towards efficient calculation of stochastic complexity, this paper proposes a novel method where multiple Gaussian components centered on each sample generated by MCMC are built and then the covariances of them are optimized so that the mixture of them approximates the Bayesian posterior distribution in terms of Kullback-Leibler divergence, which is employed also by variational Bayesian method. This paper reveals the effectiveness of the method for singular models through experimental comparison between numerical calculation results and theoretical ones. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Bayesian inference / stochastic complexity / free energy / Markov chain Monte carlo method / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 108, no. 480, NC2008-112, pp. 51-56, March 2009. | |
| Paper # | NC2008-112 | |
| Date of Issue | 2009-03-04 (NC) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | NC2008-112 |
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| Conference Information | |
| Committee | NC MBE |
| Conference Date | 2009-03-11 - 2009-03-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tamagawa Univ. |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2009-03-NC-MBE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Numerical Calculation of Stochastic Complexties through Optimization of Gaussian Mixture centered on MCMC Samples |
| Sub Title (in English) | |
| Keyword(1) | Bayesian inference |
| Keyword(2) | stochastic complexity |
| Keyword(3) | free energy |
| Keyword(4) | Markov chain Monte carlo method |
| Keyword(5) | |
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| Keyword(8) | |
| 1st Author's Name | Takayuki Higo |
| 1st Author's Affiliation | Tokyo Institute of Technology (Tokyo Inst. of Tech.) |
| 2nd Author's Name | Kenji Nagata |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Tokyo Inst. of Tech.) |
| 3rd Author's Name | Sumio Watanabe |
| 3rd Author's Affiliation | Tokyo Institute of Technology (Tokyo Inst. of Tech.) |
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| Speaker | Author-1 |
| Date Time | 2009-03-11 16:10:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2008-112 |
| Volume (vol) | vol.108 |
| Number (no) | no.480 |
| Page | pp.51-56 |
| #Pages | 6 |
| Date of Issue | 2009-03-04 (NC) |
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