Name
Title | ||
|---|---|---|
Fast Computation of the Kullback–Leibler Divergence and Exact Fisher Information for the First-Order Moving Average Model |
Abstract | ||
|---|---|---|
In this note expressions are derived that allow computation of the Kullback-Leibler (K-L) divergence between two first-order Gaussian moving average models in O n(1) time as the sample size n ?? ??. These expressions can also be used to evaluate the exact Fisher information matrix in On(1) time, and provide a basis for an asymptotic expression of the K-L divergence. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/LSP.2009.2039659 | Signal Processing Letters, IEEE |
Keywords | Field | DocType |
Gaussian processes,information theory,moving average processes,Kullback-Leibler divergence,first-order Gaussian moving average models,fisher information,Fisher information,Kullback–Leibler divergence,moving average models | Pattern recognition,Jensen–Shannon divergence,Divergence (statistics),Artificial intelligence,Fisher information,Gaussian process,Moving average,Kullback–Leibler divergence,Moving-average model,Mathematics,Autocorrelation | Journal |
Volume | Issue | ISSN |
17 | 4 | 1070-9908 |
Citations | PageRank | References |
5 | 0.55 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Enes Makalic | 1 | 55 | 11.54 |
| Daniel F. Schmidt | 2 | 51 | 10.68 |