Bayesian or Frequentist, Which Are You?
author:
Michael I. Jordan,
Department of Electrical Engineering and Computer Sciences, UC Berkeley, University of California
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| Slides | |
| 0:00 | Are You a Bayesian or a Frequentist? |
| 1:28 | Statistical Inference |
| 6:36 | Machine Learning |
| 10:11 | Decison-Theoretic Perspective -1 |
| 13:01 | Decison-Theoretic Perspective -2 |
| 18:18 | Decison-Theoretic Perspective -3 |
| 19:55 | Coherence and Calibration |
| 25:23 | The Bayesian World |
| 27:03 | Subjective Bayes |
| 34:03 | Objective Bayes |
| 36:44 | Frequentist Perspective |
| 39:20 | Frequentist Activities |
| 50:35 | Outline -1 |
| 50:50 | Surrogate Loss Functions |
| 52:02 | Motivating example: Decentralized Detection |
| 53:18 | Decentralized Detection |
| 54:26 | Decentralized Detection (cont.) |
| 56:57 | Perspectives -1 |
| 60:16 | f-divergences |
| 61:23 | Why the f-divergence? |
| 64:02 | Statistical Machine Learning Perspective |
| 64:48 | Margin-Based Loss Functions |
| 65:40 | Estimation Based on a Convex Surrogate Loss |
| 67:02 | Some Theory for Surrogate Loss Functions |
| 68:37 | Outline -2 |
| 69:38 | Setup -1 |
| 71:28 | Profiling |
| 73:43 | Some Examples |
| 74:18 | Link -1 |
| 75:00 | Conjugate Duality |
| 75:49 | Link -2 |
| 77:12 | The Easy Direction |
| 78:20 | The Direction Has a Constructive Consequence -1 |
| 79:07 | Example - Hellinger distance |
| 80:45 | Example - Variational distance |
| 81:24 | Example - Kullback-Leibler divergence |
| 81:29 | Bayes Consistency -1 |
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