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| L04.4 Combinations | 10:80 | 196 | |
| L14.9 Inferring the Unknown Bias of a Coin - Point Estimates | 9:30 | 41 | |
| L21.9 Splitting a Bernoulli Process | 5:54 | 53 | |
| L17.3 Solution to the LLMS Problem | 5:60 | 27 | |
| L19.5 CLT Examples | 13:57 | 41 | |
| L05.10 The Expected Value Rule | 10:00 | 164 | |
| S01.3 Sequences and their Limits | 6:00 | 423 | |
| L17.1 Lecture Overview | 1:41 | 50 | |
| L26.3 Review of Steady-State Behavior | 9:12 | 33 | |
| L05.3 Probability Mass Functions | 10:21 | 213 | |
| S07.2 The Variance of the Geometric | 5:42 | 98 | |
| L22.4 The Poisson PMF for the Number of Arrivals | 8:10 | 40 | |
| L02.7 Total Probability Theorem | 5:25 | 248 | |
| L22.8 The Fresh Start Property and Its Implications | 10:34 | 31 | |
| L13.10 Mean of the Sum of a Random Number of Random Variables | 6:26 | 52 | |
| L12.9 Proof of Key Properties of the Correlation Coefficient | 3:52 | 115 | |
| L15.8 Trajectory Estimation Illustration | 10:55 | 39 | |
| L05.7 Geometric Random Variables | 7:37 | 161 | |
| L10.11 Inference of the Bias of a Coin | 6:00 | 56 | |
| L14.3 Types of Inference Problems | 5:24 | 70 | |
| L05.2 Definition of Random Variables | 9:14 | 222 | |
| L03.3 Independence of Two Events | 6:10 | 227 | |
| L22.5 The Mean and Variance of the Number of Arrivals | 3:22 | 37 | |
| L25.2 Lecture Overview | 1:50 | 40 | |
| L15.6 Multiple Parameters; Trajectory Estimation | 10:33 | 33 | |
| L15.7 Linear Normal Models | 5:12 | 41 | |
| L10.7 Independent Normals | 5:36 | 64 | |
| L18.2 The Markov Inequality | 10:21 | 129 | |
| L18.6 Convergence in Probability | 8:28 | 100 | |
| L13.1 Lecture Overview | 1:47 | 67 | |
| L18.5 Polling | 8:12 | 49 | |
| L12.1 Lecture Overview | 1:29 | 63 | |
| L22.7 Time of the K-th Arrival | 10:41 | 38 | |
| L04.1 Lecture Overview | 2:29 | 222 | |
| L16.6 Example Continued: LMS Performance Evaluation | 5:30 | 33 | |
| L02.6 The Multiplication Rule | 6:17 | 273 | |
| L23.5 The Time Until the First (or last) Lightbulb Burns Out | 11:25 | 33 | |
| L15.3 Estimating a Normal Random Variable in the Presence of Additive Noise | 8:18 | 48 | |
| S01.2 De Morgan's Laws | 4:53 | 452 | |
| L13.4 Stick-Breaking Revisited | 3:53 | 53 | |
| L20.2 Overview of the Classical Statistical Framework | 11:00 | 84 | |
| L26.2 Lecture Overview | 0:40 | 33 | |
| L11.8 A Nonmonotonic Example | 7:14 | 57 | |
| L07.3 Conditional Expectation & the Total Expectation Theorem | 6:10 | 127 | |
| L24.3 Checkout Counter Example | 12:10 | 53 | |
| L24.8 Recurrent and Transient States | 5:37 | 82 | |
| L01.3 Sample Space Examples | 5:30 | 1,387 | |
| S07.1 The Inclusion-Exclusion Formula | 11:13 | 83 | |
| L12.11 Correlations Matter | 6:22 | 58 | |
| L20.7 Confidence Intervals for the Mean, When the Variance is Unknown | 6:13 | 39 | |
| L26.5 Design of a Phone System | 18:30 | 39 | |
| L24.4 Discrete-Time Finite-State Markov Chains | 7:54 | 51 | |
| L06.7 Joint PMFs and the Expected Value Rule | 10:16 | 178 | |
| S23.1 Poisson Versus Normal Approximations to the Binomial | 8:56 | 33 | |
| L04.7 Partitions | 5:20 | 158 | |
| L14.8 Inferring the Unknown Bias of a Coin and the Beta Distribution | 7:35 | 45 | |
| S14.1 The Beta Formula | 10:24 | 55 | |
| L14.7 Continuous Parameter, Continuous Observation | 3:46 | 44 | |
| L20.10 Maximum Likelihood Estimation Examples | 10:20 | 49 | |
| L11.4 A Linear Function of a Normal Random Variable | 2:45 | 53 | |
| L13.2 Conditional Expectation as a Random Variable | 4:31 | 71 | |
| S01.8 Countable and Uncountable Sets | 6:19 | 332 | |
| L25.9 Visit Frequency Interpretation of Steady-State Probabilities | 5:20 | 36 | |
| L22.2 Definition of the Poisson Process | 5:70 | 80 | |
| L23.7 Random Incidence in the Poisson Process | 9:90 | 58 | |
| L11.9 The PDF of a Function of Multiple Random Variables | 7:42 | 71 | |
| L23.4 Where is an Arrival of the Merged Process Coming From? | 5:00 | 34 | |
| L22.9 Summary of Results | 2:34 | 39 | |
| Using Questionnaires to Customize Course Content | 2:39 | 357 | |
| Using Demonstrations in Class | 2:43 | 503 | |
| Combining Chalk Talks and Slides in a Complementary Way | 2:16 | 404 | |
| Course Iteration: Incorporating Theoretical Content and Demonstrations | 2:27 | 214 | |
| The Role of Recitations | 1:44 | 306 | |
| Students' Common Misconceptions | 1:29 | 3,544 | |
| Taking a Vote to Engage Learners | 2:32 | 446 | |
| Behind-the-Scenes Demo Prep | 2:12 | 301 | |
| Tips for Physics Educators | 2:11 | 490 | |
| Making Time for Individual Questions in a Large Lecture | 1:18 | 774 | |
| Using Humor to Enhance Learning | 2:15 | 657 | |
| 8.03SC Physics III: Vibrations and Waves Introduction | 1:20 | 21,002 | |
| 21. Phased Radar, Single Electron Interference | 1:19:50 | 293 | |
| 13. Dispersive Medium, Phase Velocity, Group Velocity | 1:13:29 | 682 | |
| 6. Driven Oscillators, Resonance | 1:22:19 | 594 | |
| 18. Wave Plates, Radiation | 1:24:10 | 284 | |
| 17. Polarization, Polarizer | 1:13:25 | 301 | |
| 1. Periodic Oscillations, Harmonic Oscillators | 57:80 | 7,882 | |
| 14. Fourier Transform, AM Radio | 1:17:33 | 550 | |
| 19. Waves in Medium | 1:22:48 | 251 | |
| 8. Translation Symmetry | 1:08:42 | 372 | |
| 22. Diffraction, Resolution | 1:13:38 | 290 | |
| 20. Interference, Soap Bubble | 1:22:26 | 264 | |
| 9. Wave Equation, Standing Waves, Fourier Series | 1:15:48 | 1,848 | |
| 10. Traveling Waves | 1:13:30 | 410 | |
| 11. Sound Waves | 1:13:29 | 448 | |
| 4. Coupled Oscillators, Normal Modes | 1:17:39 | 1,073 | |
| 15. Uncertainty Principle, 2D Waves | 1:14:40 | 337 | |
| 16. 2D and 3D waves, Snell's Law | 1:21:45 | 265 | |
| 5. Beat Phenomena | 1:20:20 | 730 | |
| 2. Damped Free Oscillators | 1:16:33 | 2,028 | |
| 23. Quantum Waves and Gravitational Waves | 1:15:39 | 378 | |
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