Name
Abstract | ||
|---|---|---|
There are many scenarios in artificial intelligence, signal processing or medicine, in which a temporal sequence consists of several unknown overlapping independent causes, and we are interested in accurately recovering those canonical causes. Factorial hidden Markov models (FHMMs) present the versatility to provide a good fit to these scenarios. However, in some scenarios, the number of causes or... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TPAMI.2015.2498931 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | Field | DocType |
Hidden Markov models,Markov processes,Inference algorithms,Yttrium,Bayes methods,Computational modeling,Probability distribution | Markov process,Markov chain Monte Carlo,Pattern recognition,Computer science,Markov chain,Reversible-jump Markov chain Monte Carlo,Factorial,Artificial intelligence,Hidden Markov model,Gibbs sampling,Hidden semi-Markov model | Journal |
Volume | Issue | ISSN |
38 | 9 | 0162-8828 |
Citations | PageRank | References |
3 | 0.38 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Isabel Valera | 1 | 196 | 17.95 |
| Francisco J. R. Ruiz | 2 | 7 | 2.18 |
| Fernando Pérez-Cruz | 3 | 749 | 61.24 |