A Lower Bound on the Bayesian MSE Based on the Optimal Bias Function
| dc.creator | Ben-Haim, Zvika | |
| dc.creator | Eldar, Yonina C. | |
| dc.date | 2008-04-28 | |
| dc.date | 2009-05-27 | |
| dc.date.accessioned | 2026-07-07T13:18:01Z | |
| dc.date.available | 2026-07-07T13:18:01Z | |
| dc.description | A lower bound on the minimum mean-squared error (MSE) in a Bayesian estimation problem is proposed in this paper. This bound utilizes a well-known connection to the deterministic estimation setting. Using the prior distribution, the bias function which minimizes the Cramer-Rao bound can be determined, resulting in a lower bound on the Bayesian MSE. The bound is developed for the general case of a vector parameter with an arbitrary probability distribution, and is shown to be asymptotically tight in both the high and low signal-to-noise ratio regimes. A numerical study demonstrates several cases in which the proposed technique is both simpler to compute and tighter than alternative methods. | |
| dc.description | 18 pages, 3 figures. Accepted for publication in IEEE Transactions on Information Theory | |
| dc.identifier | https://arxiv.org/abs/0804.4391 | |
| dc.identifier | http://arxiv.org/abs/0804.4391 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/231300 | |
| dc.subject | Information Theory | |
| dc.title | A Lower Bound on the Bayesian MSE Based on the Optimal Bias Function | |
| dc.type | text |