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【英字】MIT公开课概率论

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2016-09-06 22:17:11

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。

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概率论与数理统计

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创意-发电站

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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