Главная
Study mode:
on
1
Lecture 1: Probability and Counting | Statistics 110
2
Lecture 2: Story Proofs, Axioms of Probability | Statistics 110
3
Lecture 3: Birthday Problem, Properties of Probability | Statistics 110
4
Lecture 4: Conditional Probability | Statistics 110
5
Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110
6
Lecture 6: Monty Hall, Simpson's Paradox | Statistics 110
7
Lecture 7: Gambler's Ruin and Random Variables | Statistics 110
8
Lecture 8: Random Variables and Their Distributions | Statistics 110
9
Lecture 9: Expectation, Indicator Random Variables, Linearity | Statistics 110
10
Lecture 10: Expectation Continued | Statistics 110
11
Lecture 11: The Poisson distribution | Statistics 110
12
Lecture 12: Discrete vs. Continuous, the Uniform | Statistics 110
13
Lecture 13: Normal distribution | Statistics 110
14
Lecture 14: Location, Scale, and LOTUS | Statistics 110
15
Lecture 15: Midterm Review | Statistics 110
16
Lecture 16: Exponential Distribution | Statistics 110
17
Lecture 17: Moment Generating Functions | Statistics 110
18
Lecture 18: MGFs Continued | Statistics 110
19
Lecture 19: Joint, Conditional, and Marginal Distributions | Statistics 110
20
Lecture 20: Multinomial and Cauchy | Statistics 110
21
Lecture 21: Covariance and Correlation | Statistics 110
22
Lecture 22: Transformations and Convolutions | Statistics 110
23
Lecture 23: Beta distribution | Statistics 110
24
Lecture 24: Gamma distribution and Poisson process | Statistics 110
25
Lecture 25: Order Statistics and Conditional Expectation | Statistics 110
26
Lecture 26: Conditional Expectation Continued | Statistics 110
27
Lecture 27: Conditional Expectation given an R.V. | Statistics 110
28
Lecture 28: Inequalities | Statistics 110
29
Lecture 29: Law of Large Numbers and Central Limit Theorem | Statistics 110
30
Lecture 30: Chi-Square, Student-t, Multivariate Normal | Statistics 110
31
Lecture 31: Markov Chains | Statistics 110
32
Lecture 32: Markov Chains Continued | Statistics 110
33
Lecture 33: Markov Chains Continued Further | Statistics 110
34
Lecture 34: A Look Ahead | Statistics 110
35
Joseph Blitzstein: "The Soul of Statistics" | Harvard Thinks Big 4
Description:
Dive into the world of probability with this comprehensive course from Harvard University. Explore fundamental concepts like sample spaces, conditioning, and Bayes' Theorem before delving into random variables, distributions, and limit theorems. Learn about univariate and multivariate distributions, including Normal, Binomial, Poisson, and Multivariate Normal. Discover the applications of probability in statistics, science, engineering, economics, and finance through 34 engaging lectures. Tackle over 250 practice problems with detailed solutions to reinforce your understanding. Prerequisite knowledge of single variable calculus and familiarity with matrices is recommended for this in-depth exploration of probability as a powerful tool for understanding randomness and risk in various fields.

Statistics 110 - Probability

Harvard University
Add to list
0:00 / 0:00