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【英字】MIT公开课 概率论

34.2万
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2016-09-06 22:17:11
3596
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OCW原地址: http://ocw.mit.edu/6-041SCF13 || 自压，Probabilistic Systems Analysis and Applied Probability，原地址有课件和习题。分p有编号的是正课，没编号的是seminar/tutorial。
MIT OPENCOURSEWARE
麻省理工
数学
概率论
MIT公开课
概率论与数理统计
道口老Min 发消息
男怕入错行❌号没被盗，只是金融太难了。。聊聊财经深度话题
视频选集
（44/76）
1. Probability Models and Axioms
51:12
The Probability of the Difference of Two Events
05:54
Geniuses and Chocolates
08:42
Uniform Probabilities on a Square
09:16
2. Conditioning and Bayes Rule
51:12
A Coin Tossing Puzzle
08:10
Conditional Probability Example
14:22
The Monty Hall Problem
15:59
3. Independence
46:30
A Random Walker
05:51
Communication over a Noisy Channel
19:53
Network Reliability
07:24
A Chess Tournament Problem
18:33
4. Counting
51:35
Rooks on a Chessboard
18:27
Hypergeometric Probabilities
05:48
5. Discrete Random Variables I
50:35
Sampling People on Buses
11:55
PMF of a Function of a Random Variable
15:26
6. Discrete Random Variables II
50:53
Flipping a Coin a Random Number of Times
08:42
Joint Probability Mass Function (PMF) Drill 1
17:37
The Coupon Collector Problem
07:15
7. Discrete Random Variables III
50:42
Joint Probability Mass Function (PMF) Drill 2
13:45
8. Continuous Random Variables
50:29
Calculating a Cumulative Distribution Function (CDF)
08:43
A Mixed Distribution Example
13:24
Mean & Variance of the Exponential
15:10
Normal Probability Calculation
05:24
9. Multiple Continuous Random Variables
50:51
Uniform Probabilities on a Triangle
22:58
Probability that Three Pieces Form a Triangle
12:30
The Absent Minded Professor
13:09
10. Continuous Bayes Rule; Derived Distributions
48:53
Inferring a Discrete Random Variable from a Continuous Measurement
18:36
Inferring a Continuous Random Variable from a Discrete Measurement
11:35
A Derived Distribution Example
09:30
The Probability Distribution Function (PDF) of [X]
09:05
Ambulance Travel Time
06:46
11. Derived Distributions (ctd.); Covariance
51:55
The Difference of Two Independent Exponential Random Variables
06:12
The Sum of Discrete and Continuous Random Variables
05:36
12. Iterated Expectations
47:54
The Variance in the Stick Breaking Problem
11:29
Widgets and Crates
10:06
Using the Conditional Expectation and Variance
10:10
A Random Number of Coin Flips
17:18
A Coin with Random Bias
22:58
13. Bernoulli Process
50:58
Bernoulli Process Practice
08:21
14. Poisson Process I
52:44
Competing Exponentials
07:42
15. Poisson Process II
49:28
Random Incidence Under Erlang Arrivals
09:43
16. Markov Chains I
52:06
Setting Up a Markov Chain
10:35
Markov Chain Practice 1
11:41
17. Markov Chains II
51:25
18. Markov Chains III
51:50
Mean First Passage and Recurrence Times
09:27
19. Weak Law of Large Numbers
50:13
Convergence in Probability and in the Mean Part 1
13:36
Convergence in Probability and in the Mean Part 2
05:45
Convergence in Probability Example
07:36
20. Central Limit Theorem
51:23
Probabilty Bounds
10:45
Using the Central Limit Theorem
11:24
21. Bayesian Statistical Inference I
48:50
22. Bayesian Statistical Inference II
52:16
Inferring a Parameter of Uniform Part 1
24:51
Inferring a Parameter of Uniform Part 2
19:35
An Inference Example
27:51
23. Classical Statistical Inference I
49:32
24. Classical Inference II
51:50
25. Classical Inference III
52:07

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