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#### Conditional Statistical Independence

##### Statistical Independence

A familiar example of statistical independence is the result of two tosses of a coin. Neither coin toss can influence the outcome of the other so they yield statistically independent results. More generally, if two variables are statistically independent, then neither one affects probabilities that involve the other variable. This is a very strong notion that there is “no relationship” between the two variables. This idea is formalized using conditional probability, and, to define it, some notation is now introduced that applies to the rest of this chapter.

The conditional probability that one variable, X2, has the value x2, given that (or conditional on) the fact that another variable, X1, has the value x1 is commonly denoted by

(1)

In the example of two tosses of a coin, X1 could denote the outcome of the first toss and X2 the outcome of the second toss. In this example, x1 and x2 are the values “heads” and “tails.”

In terms of conditional probability, the statistical independence of X1 and X2 is expressed by

(2)

The probability on the right side of equation (2) is just the ordinary, marginal, or unconditional probability that X2 = x2. The equality of the two probabilities in equation (2) means that the probability distribution of X2 is unaffected by the value of X1. In other words, the conditional probability is constant as a function of x1.

It is well known (for example, see Parzen, 1960) that the constant conditional probability rule in equation (2) is equivalent to the following “product rule” for joint probabilities of independent variables

(3)

The product rule means that the joint probability that X1 = x1and that X2 = x2, the left side of equation (3), is found by multiplying together the two marginal probabilities for each variable separately, the right side of equation (3). Both the constant conditional probability rule in equation (2) and the product rule in equation (3) are important for understanding the structure of latent variable models.

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