ECE 595ML Lecture 11.2: Maximum-Likelihood Estimation - Examples

By Stanley H. Chan

Purdue University

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Researchers should cite this work as follows:

  • Stanley H. Chan (2020), "ECE 595ML Lecture 11.2: Maximum-Likelihood Estimation - Examples," https://nanohub.org/resources/32445.

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WTHR 200, Purdue University, West Lafayette, IN

ECE 595ML Lecture 11.2: Maximum-Likelihood Estimation - Examples
  • Lecture 11.2: Maximum-Likelihood 1. Lecture 11.2: Maximum-Likeliho… 0
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  • Outline 2. Outline 8.70870870870871
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  • MLE does not need to be Gaussian 3. MLE does not need to be Gaussi… 11.711711711711713
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  • Bernoulli MLE 4. Bernoulli MLE 150.21688355021689
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  • Unbias and Consistent Estimator 5. Unbias and Consistent Estimato… 212.91291291291293
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  • Unbiased and Consistent Estimator 6. Unbiased and Consistent Estima… 321.88855522188857
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  • Unbiased and Consistent Estimator 7. Unbiased and Consistent Estima… 349.54954954954957
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  • From ML to Decision Boundary 8. From ML to Decision Boundary 374.50784117450786
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  • How well do you do? 9. How well do you do? 528.395061728395
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  • MLE and MAP 10. MLE and MAP 754.7881214547881
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  • From MLE to MAP 11. From MLE to MAP 797.93126459793132
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  • From MLE to MAP 12. From MLE to MAP 803.83717050383723
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  • Maximum-a-Posteriori 13. Maximum-a-Posteriori 820.88755422088764
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  • Reading List 14. Reading List 867.26726726726724
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