Review combinations and permutations on your own

A sample space, S, can be any set - finite or infinite, discrete or continuous

An event is a (measurable) subset of the sample space

Examples:

• Flipping a fair coin: S = {H, T}, P(H) = P(T) = 1/2
events are {}, {H}, {T}, {H,T} This last is the event of getting either a head or a tail, and has probability 1
• Rolling a fair die: S = {1, 2, 3, 4, 5, 6}, P(1) = P(2) = P(3) = P(4) = P(5) = P(6) = 1/6
• Choosing a card: S = 52 cards, each with a suit (S, H, D, C) and a rank (2, 3, 4, ..., 10, J, Q, K, A)
• Urn models: An urn contains balls of different colors. A ball is selected at random and recorded. It may or may not be replaced before the next ball is selected.
• Selecting a real number between 0 and 1. If all numbers have equal probabilities, then the probability of the event (a,b) with 0 < a < b < 1 is the length of the interval, b-a.
Events A and B are mutually exclusive if they have empty intersection

A probability distribution on S satisfies the following axioms:

1. P[A] is non-negative for any event A
2. P[S] = 1
3. If A and B are mutually exclusive, then P[A U B] = P[A] + P[B]
Prove: P[ complement of A] = 1 - P[A], P[empty set] = 0

On a finite sample space, the discrete uniform probability distribution gives the same probability, P[s] = 1/|S|, to each point s in S.

If S is the interval [a, b], the continuous uniform probability distribution gives probability P[ [c, d] ] = (d-c)/(b-a) to a subinterval of S.

The conditional probability of an event A with respect to an event B with non-zero probability is P[A|B] = P[A.B]/P[B], where A.B denotes the intersection of A and B.

A and B are independent if P[A.B] = P[A]P[B], so P[A|B] = P[A].

Bayes' Theorem: P[A|B] = P[A]P[B|A}/P[B]

A random variable is a real-valued function on a sample space. We will usually assume the sample space is discrete to avoid measurability problems.

A probability distribution on the sample space induces a probability density function for the random variable, X, via P[X = r] = P[s : X(s) = r]

Two random variables, X and Y are independent if P[ X = p and Y = q ] = P[X = p]P[Y = q] for all p and q.

The expected value of X, written E[X], is the weighted average of all possible values of X weighted by their probabilities.

The variance of X is Var[X] = E[ (X - E[X])2 ] = E[ X2 - 2XE[X] + E[X]2 ] = E[X2] - 2E[X]E[X] + E[X]2 = E[X2] - E[X]2

The standard deviation is the square root of the variance.

The covariance of two random variables, X and Y is

E[ (X - E[X])(Y - E[Y]) ] = E[XY] - E[X]E[Y]

If X and Y are independent this is zero, so it measures the degree of dependence between X and Y. If you normalize this by dividing by the standard deviations of X and Y, you get the correlation coefficient.