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- W418451504 abstract "In a Bayesian linear model, suppose observation y = Hx + n stems from independent inputs x and n which are Gaussian mixture (GM) distributed. With known matrix H, the minimum mean square error (MMSE) estimator for x, has analytical form. However, its performance measure, the MMSE itself, has no such closed form. Because existing Bayesian MMSE bounds prove to have limited practical value under these settings, we instead seek analytical bounds for the MMSE, both upper and lower. This paper provides such bounds, and relates them to the signal-to-noise-ratio (SNR)." @default.
- W418451504 created "2016-06-24" @default.
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- W418451504 date "2013-01-01" @default.
- W418451504 modified "2023-09-24" @default.
- W418451504 title "The Linear Model under Gaussian Mixture Inputs : Selected Problems in Communications" @default.
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