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A General Method to Devise Maximum Likelihood Signal  Form

A General Method to Devise Maximum Likelihood Signal Form

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so what are maximum likelihood and maximum a posteriori for estimation and detection and i like to think of an example so let's think of y equals a x plus n so let's think of this system where a is a constant and so i'm going to put constant and let's say n is additive white gaussian noise awgn okay so in this system we we're going to take measurements y and we're going to try to work out what x is so lots of examples of this could be for example x could be the temperature i'm just going to write make a few little notes here the temperature in an iot device for example could be measuring the temperature that might be one thing that's being that's going on here and we might be measuring it with an amplifier which is uh then going to have noise in the amplifier so maybe one example is measuring temperature in an iot device uh what about a digital signal so maybe it's uh a digital signal in noise and uh then x is the digital data uh a is the gain of the channel and n is the noise from uh

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