An information geometric approach to ML estimation with incomplete data: Application to semiblind MIMO channel identification
In this paper, we cast the stochastic maximum-likelihood estimation of parameters with incomplete data in an information geometric framework. In this vein, we develop the information geometric identification (IGID) algorithm. The algorithm consists of iterative alternating projections on two sets of probability distributions (PDs); i.e., likelihood PDs and data empirical distributions. A Gaussian assumption on the source distribution permits a closed-form low-complexity solution for these...[Show more]
|Collections||ANU Research Publications|
|Source:||IEEE Transactions on Signal Processing|
|01_Zia_An_information_geometric_2007.pdf||1.33 MB||Adobe PDF||Request a copy|
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