ECE 595ML Lecture 11.1: Maximum-Likelihood Estimation - Basic Principles

By Stanley H. Chan

Electrical and Computer Engineering, Purdue University, West Lafayette, IN

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Researchers should cite this work as follows:

  • Stanley H. Chan (2020), "ECE 595ML Lecture 11.1: Maximum-Likelihood Estimation - Basic Principles," https://nanohub.org/resources/32444.

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WTHR 200, Purdue University, West Lafayette, IN

ECE 595ML Lecture 11.1: Maximum-Likelihood Estimation - Basic Principles
  • Lecture 11.1: Maximum-Likelihood Estimation - Basic Principles 1. Lecture 11.1: Maximum-Likeliho… 0
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  • Overview 2. Overview 88.188188188188192
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  • Outline 3. Outline 116.14948281614949
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  • What is Parameter Estimation? 4. What is Parameter Estimation? 157.95795795795797
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  • MLE and MAP 5. MLE and MAP 266.332999666333
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  • Maximum Likelihood Estimation 6. Maximum Likelihood Estimation 420.22022022022026
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  • Likelihood for the Entire Dataset 7. Likelihood for the Entire Data… 507.50750750750751
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  • Maximum Likelihood Estimation 8. Maximum Likelihood Estimation 618.6186186186186
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  • Illustrating MLE when N = 1. Known σ. 9. Illustrating MLE when N = 1. K… 755.45545545545554
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  • Illustrating MLE when N = 2. Known σ. 10. Illustrating MLE when N = 2. K… 992.32565899232566
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  • Illustrating MLE when N = arbitrary integer 11. Illustrating MLE when N = arbi… 1059.6262929596264
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  • Estimation in High-dimension 12. Estimation in High-dimension 1148.3149816483151
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  • Estimation in High-dimension 13. Estimation in High-dimension 1240.8408408408409
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  • When both µ and Σ are Unknown 14. When both µ and Σ are Unknow… 1478.7454120787454
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