Name
Abstract | ||
|---|---|---|
The classical Viterbi algorithm is used to estimate the maximum likelihood state sequence from a block of observed data. It achieves this by maximising a forward path probability measure. In an analogous manner a backward path probability measure can be generated which leads to the development of a Viterbi forward-backward algorithm. This algorithm computes an ''a posteriori maximum path probability'' for each state at a given time. The resulting probability distribution across all possible state at time t can be used as a soft output for further processing. Maximising a posteriori maximum path probability at each time gives the same state sequence as obtained from the classical Viterbi algorithm. |
Year | Venue | Keywords |
|---|---|---|
1996 | ISSPA 96 - FOURTH INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND ITS APPLICATIONS, PROCEEDINGS, VOLS 1 AND 2 | forward backward algorithm,probability measure,viterbi algorithm,maximum likelihood,probability distribution,probability,maximum likelihood estimation,adaptive systems,robustness |
Field | DocType | Citations |
Forward algorithm,Pattern recognition,Expectation–maximization algorithm,Soft output Viterbi algorithm,Probability measure,Probability distribution,Artificial intelligence,Maximum likelihood sequence estimation,Iterative Viterbi decoding,Mathematics,Viterbi algorithm | Conference | 2 |
PageRank | References | Authors |
0.96 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gary D. Brushe | 1 | 3 | 2.01 |
| Robert E. Mahony | 2 | 1691 | 162.83 |
| JOHN B. MOORE | 3 | 412 | 84.61 |