MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013
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13 learners
What you'll learn
This course includes
- 31.5 hours of video
- Certificate of completion
- Access on mobile and TV
Course content
1 modules • 76 lessons • 31.5 hours of video
MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013
76 lessons
• 31.5 hours
MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013
76 lessons
• 31.5 hours
- 1. Probability Models and Axioms51:11
- 2. Conditioning and Bayes' Rule51:11
- 3. Independence46:30
- 4. Counting51:34
- 5. Discrete Random Variables I50:35
- 6. Discrete Random Variables II50:53
- 7. Discrete Random Variables III50:42
- 8. Continuous Random Variables50:29
- 9. Multiple Continuous Random Variables50:51
- 10. Continuous Bayes' Rule; Derived Distributions48:53
- 11. Derived Distributions (ctd.); Covariance51:55
- 12. Iterated Expectations47:54
- 13. Bernoulli Process50:58
- 14. Poisson Process I52:44
- 15. Poisson Process II49:28
- 16. Markov Chains I52:06
- 17. Markov Chains II51:25
- 18. Markov Chains III51:50
- 19. Weak Law of Large Numbers50:13
- 20. Central Limit Theorem51:23
- 21. Bayesian Statistical Inference I48:50
- 22. Bayesian Statistical Inference II52:16
- 23. Classical Statistical Inference I49:32
- 24. Classical Inference II51:50
- 25. Classical Inference III52:07
- The Probability of the Difference of Two Events05:55
- Geniuses and Chocolates08:43
- Uniform Probabilities on a Square09:16
- A Coin Tossing Puzzle08:11
- Conditional Probability Example14:22
- The Monty Hall Problem15:59
- A Random Walker05:52
- Communication over a Noisy Channel19:53
- Network Reliability07:24
- A Chess Tournament Problem18:33
- Rooks on a Chessboard18:28
- Hypergeometric Probabilities05:49
- Sampling People on Buses11:56
- PMF of a Function of a Random Variable15:26
- Flipping a Coin a Random Number of Times08:43
- Joint Probability Mass Function (PMF) Drill 117:37
- The Coupon Collector Problem07:15
- Joint Probability Mass Function (PMF) Drill 213:45
- Calculating a Cumulative Distribution Function (CDF)08:44
- A Mixed Distribution Example13:25
- Mean & Variance of the Exponential15:11
- Normal Probability Calculation05:25
- Uniform Probabilities on a Triangle22:58
- Probability that Three Pieces Form a Triangle12:30
- The Absent Minded Professor13:09
- Inferring a Discrete Random Variable from a Continuous Measurement18:37
- Inferring a Continuous Random Variable from a Discrete Measurement11:35
- A Derived Distribution Example09:30
- The Probability Distribution Function (PDF) of [X]09:06
- Ambulance Travel Time06:47
- The Difference of Two Independent Exponential Random Variables06:12
- The Sum of Discrete and Continuous Random Variables05:37
- The Variance in the Stick Breaking Problem11:30
- Widgets and Crates10:06
- Using the Conditional Expectation and Variance10:10
- A Random Number of Coin Flips17:19
- A Coin with Random Bias22:58
- Bernoulli Process Practice08:22
- Competing Exponentials07:42
- Random Incidence Under Erlang Arrivals09:43
- Setting Up a Markov Chain10:36
- Markov Chain Practice 111:42
- Mean First Passage and Recurrence Times09:27
- Convergence in Probability and in the Mean Part 113:37
- Convergence in Probability and in the Mean Part 205:46
- Convergence in Probability Example07:37
- Probabilty Bounds10:46
- Using the Central Limit Theorem11:25
- Inferring a Parameter of Uniform Part 124:51
- Inferring a Parameter of Uniform Part 219:35
- An Inference Example27:51
