Abstract
Hidden Markov Models are of interest in a broad set of applications including modern data driven systems involving very large data sets. However, approximate inference methods based on Bayesian averaging are precluded in such applications as each sampling step requires a full sweep over the data. We show that Approximate Bayesian Computation offers an interesting alternative for approximate sampling from the posterior distribution. In particular we use recent advances in moment based methods for HMM estimation to generate summary statistics for Approximate Bayesian Computation for large data sets offering fast access to approximate posterior samples. In a specific example we see that the new scheme is a hundred times faster than conventional Markov Chain Monte Carlo sampling using the Forward-backward method.
Original language | English |
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Title of host publication | Proceedings of the 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) |
Editors | Mamadou Mboup, Tülay Adali , Éric Moreau, Jan Larsen |
Number of pages | 6 |
Publisher | IEEE |
Publication date | 2014 |
ISBN (Print) | 978-1-4799-3694-6 |
DOIs | |
Publication status | Published - 2014 |
Event | 2014 IEEE International Workshop on Machine Learning for Signal Processing - Reims Centre des Congrès, Reims, France Duration: 21 Sept 2014 → 24 Sept 2014 Conference number: 24 https://ieeexplore.ieee.org/xpl/conhome/6945945/proceeding |
Conference
Conference | 2014 IEEE International Workshop on Machine Learning for Signal Processing |
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Number | 24 |
Location | Reims Centre des Congrès |
Country/Territory | France |
City | Reims |
Period | 21/09/2014 → 24/09/2014 |
Internet address |
Keywords
- Bioengineering
- Communication, Networking and Broadcast Technologies
- Computing and Processing
- Engineering Profession
- Signal Processing and Analysis
- Approximate Bayesian Computation
- Approximation methods
- Bayes methods
- Computational modeling
- Hidden Markov Models
- Markov Chain Monte Carlo
- Markov processes
- Moment based learning
- Monte Carlo methods
- Proposals