Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MLSP.2014.6958859 | Machine Learning for Signal Processing |
Keywords | Field | DocType |
Bayes methods,data handling,hidden Markov models,inference mechanisms,sampling methods,Bayesian averaging,HMM estimation,approximate Bayesian computation,approximate inference methods,approximate posterior samples,data driven systems,fast sampling,forward-backward method,hidden Markov model posterior,moment based methods,summary statistics,Approximate Bayesian Computation,Hidden Markov Models,Markov Chain Monte Carlo,Moment based learning | Approximate Bayesian computation,Markov process,Markov chain Monte Carlo,Computer science,Posterior probability,Approximate inference,Sampling (statistics),Artificial intelligence,Hidden Markov model,Machine learning,Gibbs sampling | Conference |
ISSN | Citations | PageRank |
2161-0363 | 0 | 0.34 |
References | Authors | |
7 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rasmus Bonnevie | 1 | 0 | 0.34 |
| Lars Kai Hansen | 2 | 2776 | 341.03 |