Algebra of Random Variables
Random variables have been introduced as functions defined on sample spaces. The simplest ones are the identity function,
It is customary to use capital (and often bold capital) letters to denote random variables. For example, on the sample space of 2 tosses of a coin the number of heads is an rv. Let it be H:
H(HH) = 2, H(HT) = H(TH) = 1, H(TT) = 0. |
The sum total S of two die is a random variable with the range
Associated with every random variable is a probability distribution (function) defined on the range of the rv:
f_{X}(t) = P(X = t). |
So, for example, f_{H} is a function defined on the set
f_{H}(0) = 1/4, f_{H}(1) = 1/2, f_{H}(2) = 1/4. |
Also,
f_{X}(x) = P(X = x) = P(x). |
All that can be done with two functions defined on the same domain, can be done with two random variables defined on the same sample space. For example, if two random variables S and T are numeric and defined on the same sample space {x} then
(S + T)(x) = S(x) + T(x). |
A constant RV can be added to any other. (An RV is constant if it takes on only one value and that - naturally enough - with the probability of 1. It may be considered as defined on any finite sample space and assigned arbitrary probabilities at every elementary outcome, subject to the sole restriction that those proabilities add up to 1.)
Also, for a function f defined on all possible values of a RV X we can define a RV
P(Y = f(x)) = P(X = x). |
- What Is Probability?
- Intuitive Probability
- Probability Problems
- Sample Spaces and Random Variables
- Probabilities
- Conditional Probability
- Dependent and Independent Events
- Algebra of Random Variables
- Expectation
- Probability Generating Functions
- Probability of Two Integers Being Coprime
- Random Walks
- Probabilistic Method
- Probability Paradoxes
- Symmetry Principle in Probability
- Non-transitive Dice
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