Timeline for Kalman Filter | Difference Between Minimizing the Mean Square Error (MMSE) & Maximizing Likelihood Value in Bayesian Estimation
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Apr 10, 2020 at 16:22 | comment | added | Royi | @GENIVI-LEARNER, Keep being generous and I will always be happy to try to assist you. If you have more questions, feel free. | |
| Apr 10, 2020 at 16:00 | comment | added | Royi | @GENIVI-LEARNER, I added the derivation for the $ {L}_{2} $ case. Yes, as I wrote above for Gaussian PDF the Mean equals the Median which equals the Mode. Actually for all symmetric distributions the Mean equals to the Median. If you add the property of Uni Modality with the Peak at the symmetric point you get Mean will equal the Median which will equal the Mode. You should read at Wikipedia - Mode. | |
| Apr 10, 2020 at 15:56 | history | edited | Royi | CC BY-SA 4.0 |
added 1163 characters in body
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| Apr 10, 2020 at 15:39 | comment | added | GENIVI-LEARNER | Also if posterior is Gaussian then does it mean the Mean, Mode and Median are all same? I really thought that the values at the tail of the gaussian curve are the Modes as they have low probability but the x's are large compared to the mean. | |
| Apr 10, 2020 at 15:36 | comment | added | GENIVI-LEARNER | Good comprehensive answer however I fail to see how is MMSE $ \left( \theta, a \right) = {\left\| \theta - a \right\|}_{2}^{2}$ is given by conditional expectation, cause expectation is just taking average given x. But the square term you are defining is the root mean square loss ${\left\| \theta - a \right\|}_{2}^{2}$ | |
| Apr 10, 2020 at 15:32 | vote | accept | GENIVI-LEARNER | ||
| Apr 9, 2020 at 22:22 | history | answered | Royi | CC BY-SA 4.0 |