Name
Abstract | ||
|---|---|---|
State estimation is studied for a special class of flag Hidden Markov Models (HMMs), which comprise 1) an arbitrary finite-state underlying Markov chain and 2) a structured observation process wherein a subset of states emit distinct flags with some probability while other states are unmeasured. For flag HMMs, an explicit computation of the probability of error for the maximum-likelihood filter an... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TSP.2016.2568167 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
Hidden Markov models,Markov processes,Error probability,Smoothing methods,Maximum likelihood estimation,Signal processing algorithms,Estimation error | Markov process,Algebraic number,Computer science,Artificial intelligence,Computation,Hidden semi-Markov model,Mathematical optimization,Markov chain,Algorithm,Filter (signal processing),Smoothing,Hidden Markov model,Machine learning | Journal |
Volume | Issue | ISSN |
64 | 17 | 1053-587X |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyle Doty | 1 | 2 | 1.06 |
| Sandip Roy | 2 | 301 | 53.03 |
| Thomas R. Fischer | 3 | 185 | 39.19 |