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Paper Abstract and Keywords
Presentation 2014-07-25 14:20
[Invited Talk] Karnel method for Bayesian inference and its applications
Kenji Fukumizu (ISM) SP2014-69
Abstract (in Japanese) (See Japanese page) 
(in English) As a kernel framework for statsitical inference, "kernel mean embedding" has been recently developed, in which probability distributions are represented by the means of feature vectors on reproducing kernel Hilbert spaces. This framework has been applied to various nonparametric inference problems such as independence tests.
In this presentation, "kernel Bayesian inference", a novel method for realizing Bayesisn inference, is introduced with the framework of kernel mean embedding. The kernel Bayesian inference provides a way of computing the kernel mean of posterior with Gram matrix operations, given the kernel mean expression of prior and conditional probability. Unlike the standard methods for Bayesian inference, no numerical integrals are needed. The kernel Bayesian inference enables us to conduct data-driven Bayesian inference in problems where it is difficult to assume an appropriate parametric model. As an application, vision-based robot localization is shown, where the kernel Bayesian inference is applied to the filtering in a state-space model.
Keyword (in Japanese) (See Japanese page) 
(in English) Bayes' rule / kernel method / nonparametric inference / / / / /  
Reference Info. IEICE Tech. Rep., vol. 114, no. 151, SP2014-69, pp. 37-40, July 2014.
Paper # SP2014-69 
Date of Issue 2014-07-17 (SP) 
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 SP2014-69

Conference Information
Committee SP IPSJ-SLP  
Conference Date 2014-07-24 - 2014-07-26 
Place (in Japanese) (See Japanese page) 
Place (in English) Hotel Hanamaki 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Speech recognition and understanding, dialog system, etc. 
Paper Information
Registration To SP 
Conference Code 2014-07-SP-SLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Karnel method for Bayesian inference and its applications 
Sub Title (in English)  
Keyword(1) Bayes' rule  
Keyword(2) kernel method  
Keyword(3) nonparametric inference  
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1st Author's Name Kenji Fukumizu  
1st Author's Affiliation The Institute of Statistical Mathematics (ISM)
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Speaker Author-1 
Date Time 2014-07-25 14:20:00 
Presentation Time 60 minutes 
Registration for SP 
Paper # SP2014-69 
Volume (vol) vol.114 
Number (no) no.151 
Page pp.37-40 
#Pages 4 
Date of Issue 2014-07-17 (SP) 

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