(PP 3.1) Random Variables - Definition and CDF
Loading...
Loading...
1,996
Loading...
Uploaded by mathematicalmonk on Jun 4, 2011
(0:00) Intuitive examples.
(1:25) Definition of a random variable.
(6:10) CDF of a random variable.
(8:28) Distribution of a random variable.
A playlist of the Probability Primer series is available here:
http://www.youtube.com/view_play_list?p=17567A1A3F5DB5E4
- 14 likes, 0 dislikes
-
Uploader Comments (mathematicalmonk)
Loading...
- 12:37
(PP 3.2) Types of Random Variablesby mathematicalmonk1,078 views
- 1:49
worst professor everby ugod355418,219 views
- 12:17
(PP 1.4) Measure theory: Examples of Measuresby mathematicalmonk3,215 views
- 3:19
Continuous vs. Discreteby mrwyrickmath1,905 views
- 14:22
(PP 6.1) Multivariate Gaussian - definitionby mathematicalmonk1,705 views
- 11:33
Continuous Random Variablesby UAMath1152,041 views
- 10:55
(ML 3.1) Decision theory (Basic Framework)by mathematicalmonk1,988 views
- 45:19
Random Variables and Probability Distributions 4by ignousocis5,691 views
- 13:39
(Info 1.1) Entropy - Definitionby mathematicalmonk4,726 views
- 11:27
(PP 1.8) Measure theory: CDFs and Borel Probability Measuresby mathematicalmonk2,455 views
- 8:56
(ML 1.1) Machine learning - overview and applicationsby mathematicalmonk6,965 views
- 10:13
(PP 5.1) Multiple discrete random variablesby mathematicalmonk880 views
- 12:26
(PP 1.R) References for Probability and Measure theoryby mathematicalmonk1,264 views
- 6:45
(PP 1.6) Measure theory: Basic Properties of Measures (continued)by mathematicalmonk2,121 views
- 13:20
(PP 5.2) Marginals and conditionalsby mathematicalmonk1,302 views
- 10:45
(PP 2.3) Independence (continued)by mathematicalmonk1,177 views
- 11:08
(PP 5.5) Law of large numbers and Central limit theoremby mathematicalmonk865 views
- 1:45
Mathematics geniusby resvol2173,585 views
- 9:50
(PP 1.2) Measure theory: Sigma-algebrasby mathematicalmonk6,812 views
- 8:40
(PP 1.5) Measure theory: Basic Properties of Measuresby mathematicalmonk3,520 views
In the definition of a random variable, what is the purpose of x as an upper boundary to X(ω)?
alkalait 5 months ago
@alkalait The condition that
{omega : X(omega)<=x} is in A
is saying that this set of omegas is measurable. (It's not saying that X(omega) is upper bounded by x.) This is a somewhat technical condition on random variables. If you are in a measure-theoretic probability class, it is important, but otherwise you don't need to worry about this --- the main thing to know is that a random variable is a function from Omega to R.
mathematicalmonk 5 months ago