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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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