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MIT 6.041SC Probabilistic Systems Analysis and Applied Probability, Fall 2013 (Youtube)
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This is a collection of 76 videos for MIT 6.041- 25 lectures videos (2010) and 51 recitation videos (2013). In the recitation videos MIT Teaching Assistants solve selected recitation and tutorial problems from the course.

View the complete course: http://ocw.mit.edu/6-041SCF13
Instructors: Qing He, Jimmy Li, Jagdish Ramakrishnan, Katie Szeto, and Kuang Xu

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
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75 videos is founds.


name
timeviews
Sampling People on Buses11:560
The Coupon Collector Problem7:150
Joint Probability Mass Function (PMF) Drill 117:370
Inferring a Parameter of Uniform Part 219:360
Inferring a Continuous Random Variable from a Discrete Measurement11:350
A Coin with Random Bias22:580
Competing Exponentials7:430
Convergence in Probability and in the Mean Part 113:370
Hypergeometric Probabilities5:490
A Mixed Distribution Example13:250
Flipping a Coin a Random Number of Times8:430
Inferring a Discrete Random Variable from a Continuous Measurement18:370
Inferring a Parameter of Uniform Part 124:520
Convergence in Probability and in the Mean Part 25:460
Mean & Variance of the Exponential15:110
Using the Central Limit Theorem11:250
Rooks on a Chessboard18:280
Network Reliability7:240
Convergence in Probability Example7:370
A Chess Tournament Problem18:330
The Probability Distribution Function (PDF) of [X]9:600
Setting Up a Markov Chain10:360
Uniform Probabilities on a Square9:170
Probabilty Bounds10:460
The Sum of Discrete and Continuous Random Variables5:370
Joint Probability Mass Function (PMF) Drill 213:450
PMF of a Function of a Random Variable15:260
The Monty Hall Problem15:590
Widgets and Crates10:600
The Difference of Two Independent Exponential Random Variables6:120
Mean First Passage and Recurrence Times9:270
Ambulance Travel Time6:470
Bernoulli Process Practice8:220
Calculating a Cumulative Distribution Function (CDF)8:440
Probability that Three Pieces Form a Triangle12:300
A Random Walker5:520
A Coin Tossing Puzzle8:110
Markov Chain Practice 111:420
Communication over a Noisy Channel19:530
Normal Probability Calculation5:250
A Random Number of Coin Flips17:190
An Inference Example27:510
Conditional Probability Example14:220
A Derived Distribution Example9:300
The Variance in the Stick Breaking Problem11:300
Geniuses and Chocolates8:430
Using the Conditional Expectation and Variance10:100
The Absent Minded Professor13:900
Uniform Probabilities on a Triangle22:580
The Probability of the Difference of Two Events5:550
7. Discrete Random Variables III50:420
4. Counting51:340
5. Discrete Random Variables I50:350
6. Discrete Random Variables II50:530
2. Conditioning and Bayes' Rule51:110
3. Independence46:300
1. Probability Models and Axioms51:110
13. Bernoulli Process50:580
24. Classical Inference II51:500
15. Poisson Process II49:280
17. Markov Chains II51:250
25. Classical Inference III52:700
19. Weak Law of Large Numbers50:130
16. Markov Chains I52:600
20. Central Limit Theorem51:230
11. Derived Distributions (ctd.); Covariance51:550
10. Continuous Bayes' Rule; Derived Distributions48:530
9. Multiple Continuous Random Variables50:510
8. Continuous Random Variables50:290
14. Poisson Process I52:440
22. Bayesian Statistical Inference II52:160
23. Classical Statistical Inference I49:320
21. Bayesian Statistical Inference I48:500
18. Markov Chains III51:500
12. Iterated Expectations47:540


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