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karlwerner01
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Can anyone help me for this question?
View attachment 9506
View attachment 9506
Maximum Likelihood (ML) estimation is a statistical method used to estimate the parameters of a probability distribution by finding the values that maximize the likelihood of the observed data.
Linear Minimum Mean Square Error (LMMSE) estimation is a signal processing technique used to estimate the unknown parameters of a signal or system by minimizing the mean square error between the estimated and true values.
ML and LMMSE estimation are both methods used to estimate unknown parameters, but they differ in their approach. ML estimation is based on the likelihood of the observed data, while LMMSE estimation is based on minimizing the mean square error.
ML and LMMSE estimation are both widely used in various fields due to their simplicity and effectiveness in estimating unknown parameters. They also have theoretical guarantees and are relatively easy to implement.
ML and LMMSE estimation have a wide range of applications, including machine learning, signal processing, communication systems, and image and video processing. They are also used in various scientific fields such as physics, biology, and economics.